diff --git a/dev/404.html b/dev/404.html index c54ac9b2b..51a70fc24 100644 --- a/dev/404.html +++ b/dev/404.html @@ -5,10 +5,10 @@ 404 | DimensionalData.jl - - + + - + @@ -18,7 +18,7 @@
- + \ No newline at end of file diff --git a/dev/api/dimensions.html b/dev/api/dimensions.html index c2772a0d2..1b8ee41bf 100644 --- a/dev/api/dimensions.html +++ b/dev/api/dimensions.html @@ -5,14 +5,14 @@ Dimensions | DimensionalData.jl - - + + - + - - - + + + @@ -20,8 +20,8 @@ -
DimensionalData.jl

Appearance

Dimensions ​

Dimensions are kept in the sub-module Dimensions.

DimensionalData.Dimensions Module
julia
Dimensions

Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.

To load Dimensions types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Dimensions

source

Dimensions have a type-hierarchy that organises plotting and dimension matching.

DimensionalData.Dimensions.Dimension Type
julia
Dimension

Abstract supertype of all dimension types.

Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.

Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.

They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.

A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.

Example:

julia
using DimensionalData, Dates
+    
DimensionalData.jl
Appearance
Dimensions ​
Dimensions are kept in the sub-module Dimensions.
DimensionalData.Dimensions Module
julia
Dimensions
Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.
To load Dimensions types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Dimensions
source
Dimensions have a type-hierarchy that organises plotting and dimension matching.
DimensionalData.Dimensions.Dimension Type
julia
Dimension
Abstract supertype of all dimension types.
Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.
Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.
They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.
A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.
Example:
julia
using DimensionalData, Dates
 
 x = X(2:2:10)
 y = Y(['a', 'b', 'c'])
@@ -73,21 +73,21 @@
   → Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   2021-01-01T00:00:00   2021-02-01T00:00:00  …   2021-12-01T00:00:00
- 4    0.0                   0.0                      0.0
source
DimensionalData.Dimensions.DependentDim Type
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
DimensionalData.Dimensions.IndependentDim Type
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
DimensionalData.Dimensions.XDim Type
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
DimensionalData.Dimensions.YDim Type
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
DimensionalData.Dimensions.ZDim Type
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
DimensionalData.Dimensions.TimeDim Type
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
DimensionalData.Dimensions.X Type
julia
X <: XDim
+ 4    0.0                   0.0                      0.0
source
DimensionalData.Dimensions.DependentDim Type
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
DimensionalData.Dimensions.IndependentDim Type
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
DimensionalData.Dimensions.XDim Type
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
DimensionalData.Dimensions.YDim Type
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
DimensionalData.Dimensions.ZDim Type
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
DimensionalData.Dimensions.TimeDim Type
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
DimensionalData.Dimensions.X Type
julia
X <: XDim
 
-X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
DimensionalData.Dimensions.Y Type
julia
Y <: YDim
+X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
DimensionalData.Dimensions.Y Type
julia
Y <: YDim
 
-Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
DimensionalData.Dimensions.Z Type
julia
Z <: ZDim
+Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
DimensionalData.Dimensions.Z Type
julia
Z <: ZDim
 
-Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
DimensionalData.Dimensions.Ti Type
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
DimensionalData.Dimensions.Dim Type
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
-custom ['a', 'b', 'c']
source
DimensionalData.Dimensions.AnonDim Type
julia
AnonDim <: Dimension
+Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
DimensionalData.Dimensions.Ti Type
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
DimensionalData.Dimensions.Dim Type
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
+custom ['a', 'b', 'c']
source
DimensionalData.Dimensions.AnonDim Type
julia
AnonDim <: Dimension
 
-AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
DimensionalData.Dimensions.@dim Macro
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
+AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
DimensionalData.Dimensions.@dim Macro
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
 using DimensionalData: @dim, YDim, XDim
 @dim Lat YDim "Latitude"
 @dim Lon XDim "Longitude"
-# output
source
Exported methods ​
These are widely useful methods for working with dimensions.
DimensionalData.Dimensions.dims Function
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+# output
source
Exported methods ​
These are widely useful methods for working with dimensions.
DimensionalData.Dimensions.dims Function
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -101,7 +101,7 @@
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
DimensionalData.Dimensions.otherdims Function
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+(↓ X, → Y)
source
DimensionalData.Dimensions.otherdims Function
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -109,7 +109,7 @@
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
DimensionalData.Dimensions.dimnum Function
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
DimensionalData.Dimensions.dimnum Function
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -118,7 +118,7 @@
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
DimensionalData.Dimensions.hasdim Function
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
DimensionalData.Dimensions.hasdim Function
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -131,13 +131,13 @@
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
Non-exported methods ​
DimensionalData.Dimensions.lookup Function
julia
lookup(x::Dimension) => Lookup
+false
source
Non-exported methods ​
DimensionalData.Dimensions.lookup Function
julia
lookup(x::Dimension) => Lookup
 lookup(x, [dims::Tuple]) => Tuple{Vararg{Lookup}}
 lookup(x::Tuple) => Tuple{Vararg{Lookup}}
-lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
DimensionalData.Dimensions.label Function
julia
label(x) => String
+lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
DimensionalData.Dimensions.label Function
julia
label(x) => String
 label(x, dims::Tuple) => NTuple{N,String}
 label(x, dim) => String
-label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.format Function
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
DimensionalData.Dimensions.dims2indices Function
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
DimensionalData.Dimensions.Lookups.selectindices Function
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
Primitive methods ​
These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.
They are not guaranteed to keep their interface, but usually will.
DimensionalData.Dimensions.commondims Function
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
+label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.format Function
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
DimensionalData.Dimensions.dims2indices Function
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
DimensionalData.Dimensions.Lookups.selectindices Function
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
Primitive methods ​
These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.
They are not guaranteed to keep their interface, but usually will.
DimensionalData.Dimensions.commondims Function
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -148,9 +148,9 @@
 (↓ X, → Z)
 
 julia> commondims(A, Ti)
-()
source
DimensionalData.Dimensions.name2dim Function
julia
name2dim(s::Symbol) => Dimension
+()
source
DimensionalData.Dimensions.name2dim Function
julia
name2dim(s::Symbol) => Dimension
 name2dim(dims...) => Tuple{Dimension,Vararg}
-name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
DimensionalData.Dimensions.reducedims Function
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
DimensionalData.Dimensions.swapdims Function
julia
swapdims(x::T, newdims) => T
+name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
DimensionalData.Dimensions.reducedims Function
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
DimensionalData.Dimensions.swapdims Function
julia
swapdims(x::T, newdims) => T
 swapdims(dims::Tuple, newdims) => Tuple{Vararg{Dimension}}
Swap dimensions for the passed in dimensions, in the order passed.
Passing in the Dimension types rewraps the dimension index, keeping the index values and metadata, while constructed Dimension objects replace the original dimension. nothing leaves the original dimension as-is.
Arguments
  • x: any object with a dims method or a Tuple of Dimension.
  • newdim: Tuple of Dimension or dimension Type.
Example
julia
using DimensionalData
 A = ones(X(2), Y(4), Z(2))
 Dimensions.swapdims(A, (Dim{:a}, Dim{:b}, Dim{:c}))
@@ -163,21 +163,21 @@
 └───────────────────────────┘
 [:, :, 1]
  1.0  1.0  1.0  1.0
- 1.0  1.0  1.0  1.0
source
DimensionalData.Dimensions.slicedims Function
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
-slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
DimensionalData.Dimensions.comparedims Function
julia
comparedims(A::AbstractDimArray...; kw...)
+ 1.0  1.0  1.0  1.0
source
DimensionalData.Dimensions.slicedims Function
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
+slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
DimensionalData.Dimensions.comparedims Function
julia
comparedims(A::AbstractDimArray...; kw...)
 comparedims(A::Tuple...; kw...)
 comparedims(A::Dimension...; kw...)
-comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
DimensionalData.Dimensions.combinedims Function
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
DimensionalData.Dimensions.sortdims Function
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
DimensionalData.Dimensions.Lookups.basetypeof Function
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
DimensionalData.Dimensions.basedims Function
julia
basedims(ds::Tuple)
-basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
DimensionalData.Dimensions.setdims Function
julia
setdims(X, newdims) => AbstractArray
+comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
DimensionalData.Dimensions.combinedims Function
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
DimensionalData.Dimensions.sortdims Function
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
DimensionalData.Dimensions.Lookups.basetypeof Function
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
DimensionalData.Dimensions.basedims Function
julia
basedims(ds::Tuple)
+basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
DimensionalData.Dimensions.setdims Function
julia
setdims(X, newdims) => AbstractArray
 setdims(::Tuple, newdims) => Tuple{Vararg{Dimension,N}}
Replaces the first dim matching <: basetypeof(newdim) with newdim, and returns a new object or tuple with the dimension updated.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • newdim: Tuple or single Dimension, Type or Symbol.
Example
julia
using DimensionalData, DimensionalData.Dimensions, DimensionalData.Lookups
 A = ones(X(10), Y(10:10:100))
 B = setdims(A, Y(Categorical('a':'j'; order=ForwardOrdered())))
 lookup(B, Y)
 # output
 Categorical{Char} ForwardOrdered
-wrapping: 'a':1:'j'
source
DimensionalData.Dimensions.dimsmatch Function
julia
dimsmatch([f], dim, query) => Bool
-dimsmatch([f], dims::Tuple, query::Tuple) => Bool
Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.
f is <: by default, but can be >: to match abstract types to concrete types.
source

-    
+wrapping: 'a':1:'j'

source

DimensionalData.Dimensions.dimsmatch Function
julia
dimsmatch([f], dim, query) => Bool
+dimsmatch([f], dims::Tuple, query::Tuple) => Bool

Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.

f is <: by default, but can be >: to match abstract types to concrete types.

source

+ \ No newline at end of file diff --git a/dev/api/lookuparrays.html b/dev/api/lookuparrays.html index f1d0ff3e8..5b4005d35 100644 --- a/dev/api/lookuparrays.html +++ b/dev/api/lookuparrays.html @@ -5,14 +5,14 @@ Lookups | DimensionalData.jl - - + + - + - - - + + + @@ -20,8 +20,8 @@ -
DimensionalData.jl

Appearance

Lookups ​

DimensionalData.Dimensions.Lookups Module
julia
Lookups

Module for Lookups and Selectors used in DimensionalData.jl

Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.

For example, these allow tracking over array order so fast indexing works even when the array is reversed.

To load Lookup types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Lookups

source

DimensionalData.Dimensions.Lookups.Lookup Type
julia
Lookup

Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.

A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.

source

DimensionalData.Dimensions.Lookups.Aligned Type
julia
Aligned <: Lookup

Abstract supertype for Lookups where the lookup is aligned with the array axes.

This is by far the most common supertype for Lookup.

source

DimensionalData.Dimensions.Lookups.AbstractSampled Type
julia
AbstractSampled <: Aligned

Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.

AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.

source

DimensionalData.Dimensions.Lookups.Sampled Type
julia
Sampled <: AbstractSampled
+    
DimensionalData.jl
Appearance
Lookups ​
DimensionalData.Dimensions.Lookups Module
julia
Lookups
Module for Lookups and Selectors used in DimensionalData.jl
Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.
For example, these allow tracking over array order so fast indexing works even when the array is reversed.
To load Lookup types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Lookups
source
DimensionalData.Dimensions.Lookups.Lookup Type
julia
Lookup
Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.
A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.
source
DimensionalData.Dimensions.Lookups.Aligned Type
julia
Aligned <: Lookup
Abstract supertype for Lookups where the lookup is aligned with the array axes.
This is by far the most common supertype for Lookup.
source
DimensionalData.Dimensions.Lookups.AbstractSampled Type
julia
AbstractSampled <: Aligned
Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.
AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.
source
DimensionalData.Dimensions.Lookups.Sampled Type
julia
Sampled <: AbstractSampled
 
 Sampled(data::AbstractVector, order::Order, span::Span, sampling::Sampling, metadata)
 Sampled(data=AutoValues(); order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata())
A concrete implementation of the Lookup AbstractSampled. It can be used to represent Points or Intervals.
Sampled is capable of representing gridded data from a wide range of sources, allowing correct bounds and Selectors for points or intervals of regular, irregular, forward and reverse lookups.
On AbstractDimArray construction, Sampled lookup is assigned for all lookups of AbstractRange not assigned to Categorical.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with Interval sampling, and Regular span for a vector with known spacing.
We set the locus of the Intervals to Start specifying that the lookup values are for the locus at the start of each interval.
julia
using DimensionalData, DimensionalData.Lookups
@@ -42,9 +42,9 @@
   80    1.0  1.0  1.0   1.0
   60    1.0  1.0  1.0   1.0
   40    1.0  1.0  1.0   1.0
-  20    1.0  1.0  1.0   1.0
source
DimensionalData.Dimensions.Lookups.AbstractCyclic Type
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
DimensionalData.Dimensions.Lookups.Cyclic Type
julia
Cyclic <: AbstractCyclic
+  20    1.0  1.0  1.0   1.0
source
DimensionalData.Dimensions.Lookups.AbstractCyclic Type
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
DimensionalData.Dimensions.Lookups.Cyclic Type
julia
Cyclic <: AbstractCyclic
 
-Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
DimensionalData.Dimensions.Lookups.AbstractCategorical Type
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
DimensionalData.Dimensions.Lookups.Categorical Type
julia
Categorical <: AbstractCategorical
+Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
DimensionalData.Dimensions.Lookups.AbstractCategorical Type
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
DimensionalData.Dimensions.Lookups.Categorical Type
julia
Categorical <: AbstractCategorical
 
 Categorical(o::Order)
 Categorical(; order=Unordered())
A Lookup where the values are categories.
This will be automatically assigned if the lookup contains AbstractString, Symbol or Char. Otherwise it can be assigned manually.
Order will be determined automatically where possible.
Arguments
  • data: An AbstractVector matching the length of the corresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. Can be provided if this is known and performance is important.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with [Interval] sampling.
julia
using DimensionalData
@@ -56,7 +56,7 @@
 # output
 
 Categorical{String} ["one", "two", "three"] Unordered,
-Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
DimensionalData.Dimensions.Lookups.Unaligned Type
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
DimensionalData.Dimensions.Lookups.Transformed Type
julia
Transformed <: Unaligned
+Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
DimensionalData.Dimensions.Lookups.Unaligned Type
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
DimensionalData.Dimensions.Lookups.Transformed Type
julia
Transformed <: Unaligned
 
 Transformed(f, dim::Dimension; metadata=NoMetadata())
Lookup that uses an affine transformation to convert dimensions from dims(lookup) to dims(array). This can be useful when the dimensions are e.g. rotated from a more commonly used axis.
Any function can be used to do the transformation, but transformations from CoordinateTransformations.jl may be useful.
Arguments
  • f: transformation function
  • dim: a dimension to transform to.
Keyword Arguments
  • metadata:
Example
julia
using DimensionalData, DimensionalData.Lookups, CoordinateTransformations
 
@@ -69,9 +69,9 @@
 da[X(At(6.0)), Y(At(2.0))]
 
 # output
-9
source
DimensionalData.Dimensions.MergedLookup Type
julia
MergedLookup <: Lookup
+9
source
DimensionalData.Dimensions.MergedLookup Type
julia
MergedLookup <: Lookup
 
-MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
DimensionalData.Dimensions.Lookups.NoLookup Type
julia
NoLookup <: Lookup
+MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
DimensionalData.Dimensions.Lookups.NoLookup Type
julia
NoLookup <: Lookup
 
 NoLookup()
A Lookup that is identical to the array axis. Selectors can't be used on this lookup.
Example
Defining a DimArray without passing lookup values to the dimensions, it will be assigned NoLookup:
julia
using DimensionalData
 
@@ -86,26 +86,26 @@
 
 # output
 
-NoLookup, NoLookup
source
DimensionalData.Dimensions.Lookups.AutoLookup Type
julia
AutoLookup <: Lookup
+NoLookup, NoLookup
source
DimensionalData.Dimensions.Lookups.AutoLookup Type
julia
AutoLookup <: Lookup
 
 AutoLookup()
-AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
DimensionalData.Dimensions.Lookups.AutoValues Type
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
The generic value getter val
DimensionalData.Dimensions.Lookups.val Function
julia
val(x)
-val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
Lookup methods:
DimensionalData.Dimensions.Lookups.bounds Function
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
+AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
DimensionalData.Dimensions.Lookups.AutoValues Type
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
The generic value getter val
DimensionalData.Dimensions.Lookups.val Function
julia
val(x)
+val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
Lookup methods:
DimensionalData.Dimensions.Lookups.bounds Function
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
 bounds(xs::Tuple) => Tuple{Vararg{Tuple{T,T}}}
 bounds(x, dim) => Tuple{T,T}
-bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
DimensionalData.Dimensions.Lookups.hasselection Function
julia
hasselection(x, selector) => Bool
-hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
DimensionalData.Dimensions.Lookups.sampling Function
julia
sampling(x, [dims::Tuple]) => Tuple
+bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
DimensionalData.Dimensions.Lookups.hasselection Function
julia
hasselection(x, selector) => Bool
+hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
DimensionalData.Dimensions.Lookups.sampling Function
julia
sampling(x, [dims::Tuple]) => Tuple
 sampling(x, dim) => Sampling
 sampling(xs::Tuple) => Tuple{Vararg{Sampling}}
-sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.span Function
julia
span(x, [dims::Tuple]) => Tuple
+sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.span Function
julia
span(x, [dims::Tuple]) => Tuple
 span(x, dim) => Span
 span(xs::Tuple) => Tuple{Vararg{Span,N}}
-span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.order Function
julia
order(x, [dims::Tuple]) => Tuple
+span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.order Function
julia
order(x, [dims::Tuple]) => Tuple
 order(xs::Tuple) => Tuple
-order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.locus Function
julia
locus(x, [dims::Tuple]) => Tuple
+order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.locus Function
julia
locus(x, [dims::Tuple]) => Tuple
 locus(x, dim) => Locus
 locus(xs::Tuple) => Tuple{Vararg{Locus,N}}
-locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.shiftlocus Function
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
Selectors ​
DimensionalData.Dimensions.Lookups.Selector Type
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
DimensionalData.Dimensions.Lookups.IntSelector Type
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
DimensionalData.Dimensions.Lookups.ArraySelector Type
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
DimensionalData.Dimensions.Lookups.At Type
julia
At <: IntSelector
+locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.Lookups.shiftlocus Function
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
Selectors ​
DimensionalData.Dimensions.Lookups.Selector Type
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
DimensionalData.Dimensions.Lookups.IntSelector Type
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
DimensionalData.Dimensions.Lookups.ArraySelector Type
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
DimensionalData.Dimensions.Lookups.At Type
julia
At <: IntSelector
 
 At(x; atol=nothing, rtol=nothing)
 At(a, b; kw...)
Selector that exactly matches the value on the passed-in dimensions, or throws an error. For ranges and arrays, every intermediate value must match an existing value - not just the end points.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Keyword atol is passed to isapprox.
Example
julia
using DimensionalData
@@ -115,7 +115,7 @@
 
 # output
 
-5
source
DimensionalData.Dimensions.Lookups.Near Type
julia
Near <: IntSelector
+5
source
DimensionalData.Dimensions.Lookups.Near Type
julia
Near <: IntSelector
 
 Near(x)
 Near(a, b)
Selector that selects the nearest index to x.
With Points this is simply the lookup values nearest to the x, however with Intervals it is the interval center nearest to x. This will be offset from the index value for Start and End locus.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between the nearsest value to each of them will be selected.
Example
julia
using DimensionalData
@@ -124,7 +124,7 @@
 A[X(Near(23)), Y(Near(5.1))]
 
 # output
-4
source
DimensionalData.Dimensions.Lookups.Between Type
julia
Between <: ArraySelector
+4
source
DimensionalData.Dimensions.Lookups.Between Type
julia
Between <: ArraySelector
 
 Between(a, b)
Depreciated: use a..b instead of Between(a, b). Other Interval objects from IntervalSets.jl, like `OpenInterval(a, b) will also work, giving the correct open/closed boundaries.
Between will e removed in future to avoid clashes with DataFrames.Between.
Selector that retrieve all indices located between 2 values, evaluated with >= for the lower value, and < for the upper value. This means the same value will not be counted twice in 2 adjacent Between selections.
For Intervals the whole interval must be lie between the values. For Points the points must fall between the values. Different Sampling types may give different results with the same input - this is the intended behaviour.
Between for Irregular intervals is a little complicated. The interval is the distance between a value and the next (for Start locus) or previous (for End locus) value.
For Center, we take the mid point between two index values as the start and end of each interval. This may or may not make sense for the values in your index, so use Between with Irregular Intervals(Center()) with caution.
Example
julia
using DimensionalData
 
@@ -140,7 +140,7 @@
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
DimensionalData.Dimensions.Lookups.Touches Type
julia
Touches <: ArraySelector
+ 20    4  5
source
DimensionalData.Dimensions.Lookups.Touches Type
julia
Touches <: ArraySelector
 
 Touches(a, b)
Selector that retrieves all indices touching the closed interval 2 values, for the maximum possible area that could interact with the supplied range.
This can be better than .. when e.g. subsetting an area to rasterize, as you may wish to include pixels that just touch the area, rather than those that fall within it.
Touches is different to using closed intervals when the lookups also contain intervals - if any of the intervals touch, they are included. With .. they are discarded unless the whole cell interval falls inside the selector interval.
Example
julia
using DimensionalData
 
@@ -155,7 +155,7 @@
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
DimensionalData.Dimensions.Lookups.Contains Type
julia
Contains <: IntSelector
+ 20    4  5
source
DimensionalData.Dimensions.Lookups.Contains Type
julia
Contains <: IntSelector
 
 Contains(x)
 Contains(a, b)
Selector that selects the interval the value is contained by. If the interval is not present in the lookup, an error will be thrown.
Can only be used for Intervals or Categorical. For Categorical it falls back to using At. Contains should not be confused with Base.contains - use Where(contains(x)) to check for if values are contain in categorical values like strings.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Example
julia
using DimensionalData; const DD = DimensionalData
@@ -165,7 +165,7 @@
 A[X(Contains(8)), Y(Contains(6.8))]
 
 # output
-3
source
DimensionalData.Dimensions.Lookups.Where Type
julia
Where <: ArraySelector
+3
source
DimensionalData.Dimensions.Lookups.Where Type
julia
Where <: ArraySelector
 
 Where(f::Function)
Selector that filters a dimension lookup by any function that accepts a single value and returns a Bool.
Example
julia
using DimensionalData
 
@@ -181,7 +181,7 @@
   → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
 └─────────────────────────────────────────────────────────────┘
   ↓ →  19  21
- 20     4   6
source
DimensionalData.Dimensions.Lookups.All Type
julia
All <: Selector
+ 20     4   6
source
DimensionalData.Dimensions.Lookups.All Type
julia
All <: Selector
 
 All(selectors::Selector...)
Selector that combines the results of other selectors. The indices used will be the union of all result sorted in ascending order.
Example
julia
using DimensionalData, Unitful
 
@@ -199,46 +199,46 @@
 └──────────────────────────────────────────────────────────────────────────────┘
   ↓ →  1 s  6 s  91 s  96 s
  10.0    1    2    19    20
- 50.0    3    6    57    60
source
Lookup traits ​
DimensionalData.Dimensions.Lookups.LookupTrait Type
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
Order ​
DimensionalData.Dimensions.Lookups.Order Type
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
DimensionalData.Dimensions.Lookups.Ordered Type
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
DimensionalData.Dimensions.Lookups.ForwardOrdered Type
julia
ForwardOrdered <: Ordered
+ 50.0    3    6    57    60
source
Lookup traits ​
DimensionalData.Dimensions.Lookups.LookupTrait Type
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
Order ​
DimensionalData.Dimensions.Lookups.Order Type
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
DimensionalData.Dimensions.Lookups.Ordered Type
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
DimensionalData.Dimensions.Lookups.ForwardOrdered Type
julia
ForwardOrdered <: Ordered
 
-ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
DimensionalData.Dimensions.Lookups.ReverseOrdered Type
julia
ReverseOrdered <: Ordered
+ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
DimensionalData.Dimensions.Lookups.ReverseOrdered Type
julia
ReverseOrdered <: Ordered
 
-ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
DimensionalData.Dimensions.Lookups.Unordered Type
julia
Unordered <: Order
+ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
DimensionalData.Dimensions.Lookups.Unordered Type
julia
Unordered <: Order
 
-Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
DimensionalData.Dimensions.Lookups.AutoOrder Type
julia
AutoOrder <: Order
+Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
DimensionalData.Dimensions.Lookups.AutoOrder Type
julia
AutoOrder <: Order
 
-AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
Span ​
DimensionalData.Dimensions.Lookups.Span Type
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
DimensionalData.Dimensions.Lookups.Regular Type
julia
Regular <: Span
+AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
Span ​
DimensionalData.Dimensions.Lookups.Span Type
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
DimensionalData.Dimensions.Lookups.Regular Type
julia
Regular <: Span
 
-Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
DimensionalData.Dimensions.Lookups.Irregular Type
julia
Irregular <: Span
+Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
DimensionalData.Dimensions.Lookups.Irregular Type
julia
Irregular <: Span
 
 Irregular(bounds::Tuple)
-Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
DimensionalData.Dimensions.Lookups.Explicit Type
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
DimensionalData.Dimensions.Lookups.AutoSpan Type
julia
AutoSpan <: Span
+Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
DimensionalData.Dimensions.Lookups.Explicit Type
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
DimensionalData.Dimensions.Lookups.AutoSpan Type
julia
AutoSpan <: Span
 
-AutoSpan()
The span will be guessed and replaced in format or set.
source
Sampling ​
DimensionalData.Dimensions.Lookups.Sampling Type
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
DimensionalData.Dimensions.Lookups.Points Type
julia
Points <: Sampling
+AutoSpan()
The span will be guessed and replaced in format or set.
source
Sampling ​
DimensionalData.Dimensions.Lookups.Sampling Type
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
DimensionalData.Dimensions.Lookups.Points Type
julia
Points <: Sampling
 
-Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
DimensionalData.Dimensions.Lookups.Intervals Type
julia
Intervals <: Sampling
+Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
DimensionalData.Dimensions.Lookups.Intervals Type
julia
Intervals <: Sampling
 
-Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
Positions ​
DimensionalData.Dimensions.Lookups.Position Type
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
DimensionalData.Dimensions.Lookups.Center Type
julia
Center <: Position
+Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
Positions ​
DimensionalData.Dimensions.Lookups.Position Type
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
DimensionalData.Dimensions.Lookups.Center Type
julia
Center <: Position
 
-Center()
Used to specify lookup values correspond to the center locus in an interval.
source
DimensionalData.Dimensions.Lookups.Start Type
julia
Start <: Position
+Center()
Used to specify lookup values correspond to the center locus in an interval.
source
DimensionalData.Dimensions.Lookups.Start Type
julia
Start <: Position
 
-Start()
Used to specify lookup values correspond to the start locus of an interval.
source
DimensionalData.Dimensions.Lookups.Begin Type
julia
Begin <: Position
+Start()
Used to specify lookup values correspond to the start locus of an interval.
source
DimensionalData.Dimensions.Lookups.Begin Type
julia
Begin <: Position
 
-Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
DimensionalData.Dimensions.Lookups.End Type
julia
End <: Position
+Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
DimensionalData.Dimensions.Lookups.End Type
julia
End <: Position
 
-End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
DimensionalData.Dimensions.Lookups.AutoPosition Type
julia
AutoPosition <: Position
+End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
DimensionalData.Dimensions.Lookups.AutoPosition Type
julia
AutoPosition <: Position
 
-AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
Metadata ​
DimensionalData.Dimensions.Lookups.AbstractMetadata Type
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
DimensionalData.Dimensions.Lookups.Metadata Type
julia
Metadata <: AbstractMetadata
+AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
Metadata ​
DimensionalData.Dimensions.Lookups.AbstractMetadata Type
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
DimensionalData.Dimensions.Lookups.Metadata Type
julia
Metadata <: AbstractMetadata
 
 Metadata{X}(val::Union{Dict,NamedTuple})
 Metadata{X}(pairs::Pair...) => Metadata{Dict}
-Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
DimensionalData.Dimensions.Lookups.NoMetadata Type
julia
NoMetadata <: AbstractMetadata
+Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
DimensionalData.Dimensions.Lookups.NoMetadata Type
julia
NoMetadata <: AbstractMetadata
 
-NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
DimensionalData.Dimensions.Lookups.units Function
julia
units(x) => Union{Nothing,Any}
+NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
DimensionalData.Dimensions.Lookups.units Function
julia
units(x) => Union{Nothing,Any}
 units(xs:Tuple) => Tuple
 unit(A::AbstractDimArray, dims::Tuple) => Tuple
-unit(A::AbstractDimArray, dim) => Union{Nothing,Any}
Get the units of an array or Dimension, or a tuple of of either.
Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source

-    
+unit(A::AbstractDimArray, dim) => Union{Nothing,Any}

Get the units of an array or Dimension, or a tuple of of either.

Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.

Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.

source

+ \ No newline at end of file diff --git a/dev/api/reference.html b/dev/api/reference.html index e4cf799e6..e293a7d5c 100644 --- a/dev/api/reference.html +++ b/dev/api/reference.html @@ -5,14 +5,14 @@ API Reference | DimensionalData.jl - - + + - + - - - + + + @@ -20,7 +20,7 @@ -
DimensionalData.jl

Appearance

API Reference ​

Arrays ​

DimensionalData.AbstractBasicDimArray Type
julia
AbstractBasicDimArray <: AbstractArray

The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension

Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.

source

DimensionalData.AbstractDimArray Type
julia
AbstractDimArray <: AbstractBasicArray

Abstract supertype for all "dim" arrays.

These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.

parent must return the source array.

They should have metadata, name and refdims methods, although these are optional.

A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.

Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"

source

DimensionalData.DimArray Type
julia
DimArray <: AbstractDimArray
+    
DimensionalData.jl
Appearance
API Reference ​
Arrays ​
DimensionalData.AbstractBasicDimArray Type
julia
AbstractBasicDimArray <: AbstractArray
The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension
Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.
source
DimensionalData.AbstractDimArray Type
julia
AbstractDimArray <: AbstractBasicArray
Abstract supertype for all "dim" arrays.
These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.
parent must return the source array.
They should have metadata, name and refdims methods, although these are optional.
A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
source
DimensionalData.DimArray Type
julia
DimArray <: AbstractDimArray
 
 DimArray(data, dims, refdims, name, metadata)
 DimArray(data, dims::Tuple; refdims=(), name=NoName(), metadata=NoMetadata())
The main concrete subtype of AbstractDimArray.
DimArray maintains and updates its Dimensions through transformations and moves dimensions to reference dimension refdims after reducing operations (like e.g. mean).
Arguments
  • data: An AbstractArray.
  • dims: A Tuple of Dimension
  • name: A string name for the array. Shows in plots and tables.
  • refdims: refence dimensions. Usually set programmatically to track past slices and reductions of dimension for labelling and reconstruction.
  • metadata: Dict or Metadata object, or NoMetadata()
Indexing can be done with all regular indices, or with Dimensions and/or Selectors.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
Example:
julia
julia> using Dates, DimensionalData
@@ -49,7 +49,7 @@
  20  0.774092
  30  0.823656
  40  0.637077
- 50  0.692235
source
Shorthand AbstractDimArray constructors:
Base.fill Function
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
+ 50  0.692235
source
Shorthand AbstractDimArray constructors:
Base.fill Function
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
 Base.fill(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray with a fill value of x.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData, Random; Random.seed!(123);
 
 julia> rand(Bool, X(2), Y(4))
@@ -59,7 +59,7 @@
   ↓ X, → Y
 └──────────────────────┘
  0  0  0  0
- 1  0  0  1
source
Base.rand Function
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
+ 1  0  0  1
source
Base.rand Function
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
 Base.rand(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Dimension...; kw...) => DimArray
Create a DimArray of random values.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
@@ -83,7 +83,7 @@
  ↓ →  100.0       150.0       200.0
   :a    0.443494    0.253849    0.867547
   :b    0.745673    0.334152    0.0802658
-  :c    0.512083    0.427328    0.311448
source
Base.zeros Function
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
+  :c    0.512083    0.427328    0.311448
source
Base.zeros Function
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
 Base.zeros(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of zeros.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> zeros(Bool, X(2), Y(4))
@@ -105,7 +105,7 @@
  ↓ →  100.0  150.0  200.0
   :a    0.0    0.0    0.0
   :b    0.0    0.0    0.0
-  :c    0.0    0.0    0.0
source
Base.ones Function
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
+  :c    0.0    0.0    0.0
source
Base.ones Function
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
 Base.ones(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of ones.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> ones(Bool, X(2), Y(4))
@@ -127,8 +127,8 @@
  ↓ →  100.0  150.0  200.0
   :a    1.0    1.0    1.0
   :b    1.0    1.0    1.0
-  :c    1.0    1.0    1.0
source
Functions for getting information from objects:
DimensionalData.Dimensions.dims Function
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+  :c    1.0    1.0    1.0
source
Functions for getting information from objects:
DimensionalData.Dimensions.dims Function
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -142,13 +142,13 @@
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
DimensionalData.Dimensions.refdims Function
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
DimensionalData.Dimensions.Lookups.metadata Function
julia
metadata(x) => (object metadata)
+(↓ X, → Y)
source
DimensionalData.Dimensions.refdims Function
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
DimensionalData.Dimensions.Lookups.metadata Function
julia
metadata(x) => (object metadata)
 metadata(x, dims::Tuple)  => Tuple (Dimension metadata)
-metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.name Function
julia
name(x) => Symbol
+metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.name Function
julia
name(x) => Symbol
 name(xs:Tuple) => NTuple{N,Symbol}
 name(x, dims::Tuple) => NTuple{N,Symbol}
-name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.otherdims Function
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
DimensionalData.Dimensions.otherdims Function
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -156,7 +156,7 @@
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
DimensionalData.Dimensions.dimnum Function
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
DimensionalData.Dimensions.dimnum Function
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -165,7 +165,7 @@
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
DimensionalData.Dimensions.hasdim Function
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
DimensionalData.Dimensions.hasdim Function
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -178,7 +178,7 @@
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
Multi-array datasets ​
DimensionalData.AbstractDimStack Type
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
DimensionalData.DimStack Type
julia
DimStack <: AbstractDimStack
+false
source
Multi-array datasets ​
DimensionalData.AbstractDimStack Type
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
DimensionalData.DimStack Type
julia
DimStack <: AbstractDimStack
 
 DimStack(data::AbstractDimArray...; kw...)
 DimStack(data::Tuple{Vararg{AbstractDimArray}}; kw...)
@@ -205,7 +205,7 @@
 (one = 4.0, two = 8.0, three = 12.0)
 
 julia> s[X(At(:a))] isa DimStack
-true
source
Dimension generators ​
DimensionalData.DimIndices Type
julia
DimIndices <: AbstractArray
+true
source
Dimension generators ​
DimensionalData.DimIndices Type
julia
DimIndices <: AbstractArray
 
 DimIndices(x)
 DimIndices(dims::Tuple)
@@ -242,7 +242,7 @@
 └─────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'c'
  0.0  0.9063    0.0991336
- 0.6  0.745673  0.692209
source
DimensionalData.DimSelectors Type
julia
DimSelectors <: AbstractArray
+ 0.6  0.745673  0.692209
source
DimensionalData.DimSelectors Type
julia
DimSelectors <: AbstractArray
 
 DimSelectors(x; selectors, atol...)
 DimSelectors(dims::Tuple; selectors, atol...)
@@ -260,11 +260,11 @@
   ↓ →  1.0        11.0       21.0
   1.0  0.691162    0.218579   0.539076
  10.0  0.0303789   0.420756   0.485687
- 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
DimensionalData.DimPoints Type
julia
DimPoints <: AbstractArray
+ 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
DimensionalData.DimPoints Type
julia
DimPoints <: AbstractArray
 
 DimPoints(x; order)
 DimPoints(dims::Tuple; order)
-DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
Tables.jl/TableTraits.jl interface ​
DimensionalData.AbstractDimTable Type
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
DimensionalData.DimTable Type
julia
DimTable <: AbstractDimTable
+DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
Tables.jl/TableTraits.jl interface ​
DimensionalData.AbstractDimTable Type
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
DimensionalData.DimTable Type
julia
DimTable <: AbstractDimTable
 
 DimTable(s::AbstractDimStack; mergedims=nothing)
 DimTable(x::AbstractDimArray; layersfrom=nothing, mergedims=nothing)
@@ -294,7 +294,7 @@
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0     1.0  1.0  1.0  1.0  1.0  1.0  1.0
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  …  1.0  1.0  1.0  1.0  1.0  1.0  1.0
 
-julia>
source
Group by methods ​
For transforming DimensionalData objects:
DataAPI.groupby Function
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
+julia>
source
Group by methods ​
For transforming DimensionalData objects:
DataAPI.groupby Function
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
 groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Dimension{<:Callable}...)
Group A by grouping functions or Bins over multiple dimensions.
Arguments
  • A: any AbstractDimArray or AbstractDimStack.
  • dims: Pairs such as groups = groupby(A, :dimname => groupingfunction) or wrapped Dimensions like groups = groupby(A, DimType(groupingfunction)). Instead of a grouping function Bins can be used to specify group bins.
Return value
A DimGroupByArray is returned, which is basically a regular AbstractDimArray but holding the grouped AbstractDimArray or AbstractDimStack. Its dims hold the sorted values returned by the grouping function/s.
Base julia and package methods work on DimGroupByArray as for any other AbstractArray of AbstractArray.
It is common to broadcast or map a reducing function over groups, such as mean or sum, like mean.(groups) or map(mean, groups). This will return a regular DimArray, or DimGroupByArray if dims keyword is used in the reducing function or it otherwise returns an AbstractDimArray or AbstractDimStack.
Example
Group some data along the time dimension:
julia
julia> using DimensionalData, Dates
 
 julia> A = rand(X(1:0.1:20), Y(1:20), Ti(DateTime(2000):Day(3):DateTime(2003)));
@@ -347,8 +347,8 @@
   ⋮
  10        0.501105     0.500644
  11        0.498606     0.498801
- 12        0.501643     0.499298
source
DimensionalData.DimGroupByArray Type
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
DimensionalData.Bins Type
julia
Bins(f, bins; labels, pad)
-Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
DimensionalData.ranges Function
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
DimensionalData.intervals Function
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
DimensionalData.CyclicBins Type
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
DimensionalData.seasons Function
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
DimensionalData.months Function
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
DimensionalData.hours Function
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
Utility methods ​
For transforming DimensionalData objects:
DimensionalData.Dimensions.Lookups.set Function
julia
set(x, val)
+ 12        0.501643     0.499298
source
DimensionalData.DimGroupByArray Type
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
DimensionalData.Bins Type
julia
Bins(f, bins; labels, pad)
+Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
DimensionalData.ranges Function
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
DimensionalData.intervals Function
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
DimensionalData.CyclicBins Type
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
DimensionalData.seasons Function
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
DimensionalData.months Function
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
DimensionalData.hours Function
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
Utility methods ​
For transforming DimensionalData objects:
DimensionalData.Dimensions.Lookups.set Function
julia
set(x, val)
 set(x, args::Pairs...) => x with updated field/s
 set(x, args...; kw...) => x with updated field/s
 set(x, args::Tuple{Vararg{Dimension}}; kw...) => x with updated field/s
@@ -409,11 +409,11 @@
   ↓ →  -20.0  -10.0  0.0  10.0
  10.0    0.0    0.0  0.0   0.0
  20.0    0.0    0.0  0.0   0.0
- 30.0    0.0    0.0  0.0   0.0
source
DimensionalData.Dimensions.Lookups.rebuild Function
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
DimensionalData.modify Function
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
+ 30.0    0.0    0.0  0.0   0.0
source
DimensionalData.Dimensions.Lookups.rebuild Function
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
DimensionalData.modify Function
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
 modify(f, s::AbstractDimStack) => AbstractDimStack
 modify(f, dim::Dimension) => Dimension
 modify(f, x, lookupdim::Dimension) => typeof(x)
Modify the parent data, rebuilding the object wrapper without change. f must return a AbstractArray of the same size as the original.
This method is mostly useful as a way of swapping the parent array type of an object.
Example
If we have a previously-defined DimArray, we can copy it to an Nvidia GPU with:
julia
A = DimArray(rand(100, 100), (X, Y))
-modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
DimensionalData.@d Macro
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
+modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
DimensionalData.@d Macro
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
 da1 = ones(X(3))
 da2 = fill(2, Y(4), X(3))
 
@@ -425,7 +425,7 @@
 
 @d @. da1 * da2
 # Use parentheses areound `@.` if you need to pass options
-@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
DimensionalData.broadcast_dims Function
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
DimensionalData.broadcast_dims! Function
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
DimensionalData.mergedims Function
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
+@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
DimensionalData.broadcast_dims Function
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
DimensionalData.broadcast_dims! Function
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
DimensionalData.mergedims Function
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
 
 julia> ds = (X(0:0.1:0.4), Y(10:10:100), Ti([0, 3, 4]))
 (↓ X  0.0:0.1:0.4,
@@ -434,9 +434,9 @@
 
 julia> mergedims(ds, (X, Y) => :space)
 (↓ Ti    [0, 3, 4],
-→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
-mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
DimensionalData.unmergedims Function
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
-unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
DimensionalData.reorder Function
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
+→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
+mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
DimensionalData.unmergedims Function
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
+unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
DimensionalData.reorder Function
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
 reorder(A::Union{AbstractDimArray,AbstractDimStack}, order)
 reorder(A::Dimension, order::Order)
Reorder every dims index/array to order, or reorder index for the given dimension(s) in order.
order can be an Order, Dimension => Order pairs. A Tuple of Dimensions or any object that defines dims can be used in which case the dimensions of this object are used for reordering.
If no axis reversal is required the same objects will be returned, without allocation.
Example
julia
using DimensionalData
 
@@ -450,7 +450,7 @@
 reorder(rev, da) == da
 
 # output
-true
source
Global lookup strictness settings ​
Control how strict DimensionalData when comparing Lookups before doing broadcasts and matrix multipications.
In some cases (especially DimVector and small DimArray) checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.
DimensionalData.strict_broadcast Function
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_broadcast! Function
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_matmul Function
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_matmul! Function
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
Base methods
Base.cat Function
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
Base.copy! Function
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
Base.eachslice Function
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
+true
source
Global lookup strictness settings ​
Control how strict DimensionalData when comparing Lookups before doing broadcasts and matrix multipications.
In some cases (especially DimVector and small DimArray) checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.
DimensionalData.strict_broadcast Function
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_broadcast! Function
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_matmul Function
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
DimensionalData.strict_matmul! Function
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
Base methods
Base.cat Function
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
Base.copy! Function
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
Base.eachslice Function
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
            x=DimArray(randn(2, 3, 4), (X([:x1, :x2]), Y(1:3), Z)),
            y=DimArray(randn(2, 3, 5), (X([:x1, :x2]), Y(1:3), Ti))
        ));
@@ -473,17 +473,17 @@
 ├──────────────────────────────────────────────── layers ┤
   :x eltype: Float64 dims: Y size: 3
   :y eltype: Float64 dims: Y, Ti size: 3×5
-└────────────────────────────────────────────────────────┘
source
Most base methods work as expected, using Dimension wherever a dims keyword is used. They are not all specifically documented here.
Name ​
DimensionalData.AbstractName Type
julia
AbstractName
Abstract supertype for name wrappers.
source
DimensionalData.Name Type
julia
Name <: AbstractName
+└────────────────────────────────────────────────────────┘
source
Most base methods work as expected, using Dimension wherever a dims keyword is used. They are not all specifically documented here.
Name ​
DimensionalData.AbstractName Type
julia
AbstractName
Abstract supertype for name wrappers.
source
DimensionalData.Name Type
julia
Name <: AbstractName
 
 Name(name::Union{Symbol,Name) => Name
-Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
DimensionalData.NoName Type
julia
NoName <: AbstractName
+Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
DimensionalData.NoName Type
julia
NoName <: AbstractName
 
-NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
Internal interface ​
DimensionalData.DimArrayInterface Type
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
DimensionalData.DimStackInterface Type
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
DimensionalData.rebuild_from_arrays Function
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
DimensionalData.show_main Function
julia
show_main(io::IO, mime, A::AbstractDimArray)
-show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
DimensionalData.show_after Function
julia
show_after(io::IO, mime, A::AbstractDimArray)
-show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
DimensionalData.refdims_title Function
julia
refdims_title(A::AbstractDimArray)
+NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
Internal interface ​
DimensionalData.DimArrayInterface Type
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
DimensionalData.DimStackInterface Type
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
DimensionalData.rebuild_from_arrays Function
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
DimensionalData.show_main Function
julia
show_main(io::IO, mime, A::AbstractDimArray)
+show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
DimensionalData.show_after Function
julia
show_after(io::IO, mime, A::AbstractDimArray)
+show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
DimensionalData.refdims_title Function
julia
refdims_title(A::AbstractDimArray)
 refdims_title(refdims::Tuple)
-refdims_title(refdim::Dimension)
Generate a title string based on reference dimension values.
source

-    
+refdims_title(refdim::Dimension)

Generate a title string based on reference dimension values.

source

+ \ No newline at end of file diff --git a/dev/assets/api_dimensions.md.SBrxkSyF.js b/dev/assets/api_dimensions.md.0EpvcW_-.js similarity index 97% rename from dev/assets/api_dimensions.md.SBrxkSyF.js rename to dev/assets/api_dimensions.md.0EpvcW_-.js index c54b7eba0..da9b460d2 100644 --- a/dev/assets/api_dimensions.md.SBrxkSyF.js +++ b/dev/assets/api_dimensions.md.0EpvcW_-.js @@ -1,5 +1,5 @@ -import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framework.B8l2DGq3.js";const ss=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),d={name:"api/dimensions.md"},o={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""};function $(K,s,W,J,H,Q){const n=h("Badge");return k(),p("div",null,[s[108]||(s[108]=i("h1",{id:"dimensions",tabindex:"-1"},[a("Dimensions "),i("a",{class:"header-anchor",href:"#dimensions","aria-label":'Permalink to "Dimensions"'},"​")],-1)),s[109]||(s[109]=i("p",null,[a("Dimensions are kept in the sub-module "),i("code",null,"Dimensions"),a(".")],-1)),i("details",o,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions",href:"#DimensionalData.Dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Dimensions

Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.

To load Dimensions types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Dimensions

source

`,5))]),s[110]||(s[110]=i("p",null,"Dimensions have a type-hierarchy that organises plotting and dimension matching.",-1)),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Dimension",href:"#DimensionalData.Dimensions.Dimension"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dimension")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t(`
julia
Dimension

Abstract supertype of all dimension types.

Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.

Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.

They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.

A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.

Example:

julia
using DimensionalData, Dates
+import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framework.BAAK0EyE.js";const ss=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),d={name:"api/dimensions.md"},o={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""};function $(K,s,W,J,H,Q){const n=h("Badge");return k(),p("div",null,[s[108]||(s[108]=i("h1",{id:"dimensions",tabindex:"-1"},[a("Dimensions "),i("a",{class:"header-anchor",href:"#dimensions","aria-label":'Permalink to "Dimensions"'},"​")],-1)),s[109]||(s[109]=i("p",null,[a("Dimensions are kept in the sub-module "),i("code",null,"Dimensions"),a(".")],-1)),i("details",o,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions",href:"#DimensionalData.Dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Dimensions
Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.
To load Dimensions types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Dimensions
source
`,5))]),s[110]||(s[110]=i("p",null,"Dimensions have a type-hierarchy that organises plotting and dimension matching.",-1)),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Dimension",href:"#DimensionalData.Dimensions.Dimension"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dimension")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t(`
julia
Dimension
Abstract supertype of all dimension types.
Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.
Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.
They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.
A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.
Example:
julia
using DimensionalData, Dates
 
 x = X(2:2:10)
 y = Y(['a', 'b', 'c'])
@@ -51,21 +51,21 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
   → Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   2021-01-01T00:00:00   2021-02-01T00:00:00  …   2021-12-01T00:00:00
- 4    0.0                   0.0                      0.0
source
`,13))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.DependentDim",href:"#DimensionalData.Dimensions.DependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.DependentDim")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
',3))]),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.IndependentDim",href:"#DimensionalData.Dimensions.IndependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.IndependentDim")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
',3))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.XDim",href:"#DimensionalData.Dimensions.XDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.XDim")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t('
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
',3))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.YDim",href:"#DimensionalData.Dimensions.YDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.YDim")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
',3))]),i("details",m,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.ZDim",href:"#DimensionalData.Dimensions.ZDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.ZDim")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t('
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
',3))]),i("details",F,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.TimeDim",href:"#DimensionalData.Dimensions.TimeDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.TimeDim")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
',4))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.X",href:"#DimensionalData.Dimensions.X"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.X")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
X <: XDim
+ 4    0.0                   0.0                      0.0
source
`,13))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.DependentDim",href:"#DimensionalData.Dimensions.DependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.DependentDim")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
',3))]),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.IndependentDim",href:"#DimensionalData.Dimensions.IndependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.IndependentDim")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
',3))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.XDim",href:"#DimensionalData.Dimensions.XDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.XDim")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t('
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
',3))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.YDim",href:"#DimensionalData.Dimensions.YDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.YDim")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
',3))]),i("details",m,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.ZDim",href:"#DimensionalData.Dimensions.ZDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.ZDim")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t('
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
',3))]),i("details",F,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.TimeDim",href:"#DimensionalData.Dimensions.TimeDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.TimeDim")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
',4))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.X",href:"#DimensionalData.Dimensions.X"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.X")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
X <: XDim
 
-X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
`,7))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Y",href:"#DimensionalData.Dimensions.Y"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Y")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t(`
julia
Y <: YDim
+X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
`,7))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Y",href:"#DimensionalData.Dimensions.Y"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Y")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t(`
julia
Y <: YDim
 
-Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
`,7))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Z",href:"#DimensionalData.Dimensions.Z"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Z")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Z <: ZDim
+Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
`,7))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Z",href:"#DimensionalData.Dimensions.Z"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Z")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Z <: ZDim
 
-Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
`,7))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.Ti",href:"#DimensionalData.Dimensions.Ti"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Ti")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t('
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
',9))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Dim",href:"#DimensionalData.Dimensions.Dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dim")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
-custom ['a', 'b', 'c']
source
`,5))]),i("details",j,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.AnonDim",href:"#DimensionalData.Dimensions.AnonDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.AnonDim")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AnonDim <: Dimension
+Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
`,7))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.Ti",href:"#DimensionalData.Dimensions.Ti"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Ti")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t('
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
',9))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Dim",href:"#DimensionalData.Dimensions.Dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dim")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
+custom ['a', 'b', 'c']
source
`,5))]),i("details",j,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.AnonDim",href:"#DimensionalData.Dimensions.AnonDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.AnonDim")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AnonDim <: Dimension
 
-AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
`,3))]),i("details",B,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.@dim",href:"#DimensionalData.Dimensions.@dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.@dim")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[44]||(s[44]=t(`
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
+AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
`,3))]),i("details",B,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.@dim",href:"#DimensionalData.Dimensions.@dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.@dim")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[44]||(s[44]=t(`
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
 using DimensionalData: @dim, YDim, XDim
 @dim Lat YDim "Latitude"
 @dim Lon XDim "Longitude"
-# output
source
`,8))]),s[111]||(s[111]=i("h3",{id:"Exported-methods",tabindex:"-1"},[a("Exported methods "),i("a",{class:"header-anchor",href:"#Exported-methods","aria-label":'Permalink to "Exported methods {#Exported-methods}"'},"​")],-1)),s[112]||(s[112]=i("p",null,"These are widely useful methods for working with dimensions.",-1)),i("details",v,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.dims-api-dimensions",href:"#DimensionalData.Dimensions.dims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+# output
source
`,8))]),s[111]||(s[111]=i("h3",{id:"Exported-methods",tabindex:"-1"},[a("Exported methods "),i("a",{class:"header-anchor",href:"#Exported-methods","aria-label":'Permalink to "Exported methods {#Exported-methods}"'},"​")],-1)),s[112]||(s[112]=i("p",null,"These are widely useful methods for working with dimensions.",-1)),i("details",v,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.dims-api-dimensions",href:"#DimensionalData.Dimensions.dims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -79,7 +79,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
`,13))]),i("details",A,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.otherdims-api-dimensions",href:"#DimensionalData.Dimensions.otherdims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+(↓ X, → Y)
source
`,13))]),i("details",A,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.otherdims-api-dimensions",href:"#DimensionalData.Dimensions.otherdims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -87,7 +87,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
`,8))]),i("details",T,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.dimnum-api-dimensions",href:"#DimensionalData.Dimensions.dimnum-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
`,8))]),i("details",T,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.dimnum-api-dimensions",href:"#DimensionalData.Dimensions.dimnum-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -96,7 +96,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
`,8))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.hasdim-api-dimensions",href:"#DimensionalData.Dimensions.hasdim-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
`,8))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.hasdim-api-dimensions",href:"#DimensionalData.Dimensions.hasdim-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -109,13 +109,13 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
`,8))]),s[113]||(s[113]=i("h3",{id:"Non-exported-methods",tabindex:"-1"},[a("Non-exported methods "),i("a",{class:"header-anchor",href:"#Non-exported-methods","aria-label":'Permalink to "Non-exported methods {#Non-exported-methods}"'},"​")],-1)),i("details",w,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.lookup",href:"#DimensionalData.Dimensions.lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.lookup")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
lookup(x::Dimension) => Lookup
+false
source
`,8))]),s[113]||(s[113]=i("h3",{id:"Non-exported-methods",tabindex:"-1"},[a("Non-exported methods "),i("a",{class:"header-anchor",href:"#Non-exported-methods","aria-label":'Permalink to "Non-exported methods {#Non-exported-methods}"'},"​")],-1)),i("details",w,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.lookup",href:"#DimensionalData.Dimensions.lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.lookup")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
lookup(x::Dimension) => Lookup
 lookup(x, [dims::Tuple]) => Tuple{Vararg{Lookup}}
 lookup(x::Tuple) => Tuple{Vararg{Lookup}}
-lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
`,5))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.label",href:"#DimensionalData.Dimensions.label"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.label")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
label(x) => String
+lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
`,5))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.label",href:"#DimensionalData.Dimensions.label"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.label")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
label(x) => String
 label(x, dims::Tuple) => NTuple{N,String}
 label(x, dim) => String
-label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",q,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.format",href:"#DimensionalData.Dimensions.format"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.format")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t('
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
',5))]),i("details",X,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.dims2indices",href:"#DimensionalData.Dimensions.dims2indices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims2indices")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
',3))]),i("details",Y,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.selectindices",href:"#DimensionalData.Dimensions.Lookups.selectindices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.selectindices")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[71]||(s[71]=t('
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
',3))]),s[114]||(s[114]=i("h3",{id:"Primitive-methods",tabindex:"-1"},[a("Primitive methods "),i("a",{class:"header-anchor",href:"#Primitive-methods","aria-label":'Permalink to "Primitive methods {#Primitive-methods}"'},"​")],-1)),s[115]||(s[115]=i("p",null,"These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.",-1)),s[116]||(s[116]=i("p",null,"They are not guaranteed to keep their interface, but usually will.",-1)),i("details",O,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.commondims",href:"#DimensionalData.Dimensions.commondims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.commondims")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=t(`
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
+label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",q,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.format",href:"#DimensionalData.Dimensions.format"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.format")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t('
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
',5))]),i("details",X,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.dims2indices",href:"#DimensionalData.Dimensions.dims2indices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims2indices")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
',3))]),i("details",Y,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.selectindices",href:"#DimensionalData.Dimensions.Lookups.selectindices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.selectindices")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[71]||(s[71]=t('
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
',3))]),s[114]||(s[114]=i("h3",{id:"Primitive-methods",tabindex:"-1"},[a("Primitive methods "),i("a",{class:"header-anchor",href:"#Primitive-methods","aria-label":'Permalink to "Primitive methods {#Primitive-methods}"'},"​")],-1)),s[115]||(s[115]=i("p",null,"These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.",-1)),s[116]||(s[116]=i("p",null,"They are not guaranteed to keep their interface, but usually will.",-1)),i("details",O,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.commondims",href:"#DimensionalData.Dimensions.commondims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.commondims")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=t(`
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -126,9 +126,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (↓ X, → Z)
 
 julia> commondims(A, Ti)
-()
source
`,6))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.name2dim",href:"#DimensionalData.Dimensions.name2dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name2dim")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=t(`
julia
name2dim(s::Symbol) => Dimension
+()
source
`,6))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.name2dim",href:"#DimensionalData.Dimensions.name2dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name2dim")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=t(`
julia
name2dim(s::Symbol) => Dimension
 name2dim(dims...) => Tuple{Dimension,Vararg}
-name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
`,4))]),i("details",S,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.reducedims",href:"#DimensionalData.Dimensions.reducedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.reducedims")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[80]||(s[80]=t('
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
',4))]),i("details",Z,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.swapdims",href:"#DimensionalData.Dimensions.swapdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.swapdims")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=t(`
julia
swapdims(x::T, newdims) => T
+name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
`,4))]),i("details",S,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.reducedims",href:"#DimensionalData.Dimensions.reducedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.reducedims")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[80]||(s[80]=t('
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
',4))]),i("details",Z,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.swapdims",href:"#DimensionalData.Dimensions.swapdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.swapdims")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=t(`
julia
swapdims(x::T, newdims) => T
 swapdims(dims::Tuple, newdims) => Tuple{Vararg{Dimension}}
Swap dimensions for the passed in dimensions, in the order passed.
Passing in the Dimension types rewraps the dimension index, keeping the index values and metadata, while constructed Dimension objects replace the original dimension. nothing leaves the original dimension as-is.
Arguments
  • x: any object with a dims method or a Tuple of Dimension.
  • newdim: Tuple of Dimension or dimension Type.
Example
julia
using DimensionalData
 A = ones(X(2), Y(4), Z(2))
 Dimensions.swapdims(A, (Dim{:a}, Dim{:b}, Dim{:c}))
@@ -141,17 +141,17 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 └───────────────────────────┘
 [:, :, 1]
  1.0  1.0  1.0  1.0
- 1.0  1.0  1.0  1.0
source
`,8))]),i("details",I,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.slicedims",href:"#DimensionalData.Dimensions.slicedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.slicedims")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=t(`
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
-slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
`,7))]),i("details",N,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.comparedims",href:"#DimensionalData.Dimensions.comparedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.comparedims")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=t(`
julia
comparedims(A::AbstractDimArray...; kw...)
+ 1.0  1.0  1.0  1.0
source
`,8))]),i("details",I,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.slicedims",href:"#DimensionalData.Dimensions.slicedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.slicedims")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=t(`
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
+slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
`,7))]),i("details",N,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.comparedims",href:"#DimensionalData.Dimensions.comparedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.comparedims")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=t(`
julia
comparedims(A::AbstractDimArray...; kw...)
 comparedims(A::Tuple...; kw...)
 comparedims(A::Dimension...; kw...)
-comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
`,8))]),i("details",P,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.combinedims",href:"#DimensionalData.Dimensions.combinedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.combinedims")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=t('
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
',4))]),i("details",V,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.sortdims",href:"#DimensionalData.Dimensions.sortdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.sortdims")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=t('
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
',5))]),i("details",M,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.basetypeof",href:"#DimensionalData.Dimensions.Lookups.basetypeof"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.basetypeof")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=t('
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
',4))]),i("details",R,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.basedims",href:"#DimensionalData.Dimensions.basedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.basedims")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[101]||(s[101]=t(`
julia
basedims(ds::Tuple)
-basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
`,4))]),i("details",G,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.setdims",href:"#DimensionalData.Dimensions.setdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.setdims")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=t(`
julia
setdims(X, newdims) => AbstractArray
+comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
`,8))]),i("details",P,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.combinedims",href:"#DimensionalData.Dimensions.combinedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.combinedims")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=t('
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
',4))]),i("details",V,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.sortdims",href:"#DimensionalData.Dimensions.sortdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.sortdims")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=t('
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
',5))]),i("details",M,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.basetypeof",href:"#DimensionalData.Dimensions.Lookups.basetypeof"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.basetypeof")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=t('
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
',4))]),i("details",R,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.basedims",href:"#DimensionalData.Dimensions.basedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.basedims")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[101]||(s[101]=t(`
julia
basedims(ds::Tuple)
+basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
`,4))]),i("details",G,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.setdims",href:"#DimensionalData.Dimensions.setdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.setdims")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=t(`
julia
setdims(X, newdims) => AbstractArray
 setdims(::Tuple, newdims) => Tuple{Vararg{Dimension,N}}
Replaces the first dim matching <: basetypeof(newdim) with newdim, and returns a new object or tuple with the dimension updated.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • newdim: Tuple or single Dimension, Type or Symbol.
Example
julia
using DimensionalData, DimensionalData.Dimensions, DimensionalData.Lookups
 A = ones(X(10), Y(10:10:100))
 B = setdims(A, Y(Categorical('a':'j'; order=ForwardOrdered())))
 lookup(B, Y)
 # output
 Categorical{Char} ForwardOrdered
-wrapping: 'a':1:'j'
source
`,7))]),i("details",U,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.dimsmatch",href:"#DimensionalData.Dimensions.dimsmatch"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimsmatch")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=t(`
julia
dimsmatch([f], dim, query) => Bool
-dimsmatch([f], dims::Tuple, query::Tuple) => Bool
Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.
f is <: by default, but can be >: to match abstract types to concrete types.
source
`,4))])])}const is=l(d,[["render",$]]);export{ss as __pageData,is as default};
+wrapping: 'a':1:'j'

source

`,7))]),i("details",U,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.dimsmatch",href:"#DimensionalData.Dimensions.dimsmatch"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimsmatch")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=t(`
julia
dimsmatch([f], dim, query) => Bool
+dimsmatch([f], dims::Tuple, query::Tuple) => Bool

Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.

f is <: by default, but can be >: to match abstract types to concrete types.

source

`,4))])])}const is=l(d,[["render",$]]);export{ss as __pageData,is as default}; diff --git a/dev/assets/api_dimensions.md.SBrxkSyF.lean.js b/dev/assets/api_dimensions.md.0EpvcW_-.lean.js similarity index 97% rename from dev/assets/api_dimensions.md.SBrxkSyF.lean.js rename to dev/assets/api_dimensions.md.0EpvcW_-.lean.js index c54b7eba0..da9b460d2 100644 --- a/dev/assets/api_dimensions.md.SBrxkSyF.lean.js +++ b/dev/assets/api_dimensions.md.0EpvcW_-.lean.js @@ -1,5 +1,5 @@ -import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framework.B8l2DGq3.js";const ss=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),d={name:"api/dimensions.md"},o={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""};function $(K,s,W,J,H,Q){const n=h("Badge");return k(),p("div",null,[s[108]||(s[108]=i("h1",{id:"dimensions",tabindex:"-1"},[a("Dimensions "),i("a",{class:"header-anchor",href:"#dimensions","aria-label":'Permalink to "Dimensions"'},"​")],-1)),s[109]||(s[109]=i("p",null,[a("Dimensions are kept in the sub-module "),i("code",null,"Dimensions"),a(".")],-1)),i("details",o,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions",href:"#DimensionalData.Dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Dimensions

Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.

To load Dimensions types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Dimensions

source

`,5))]),s[110]||(s[110]=i("p",null,"Dimensions have a type-hierarchy that organises plotting and dimension matching.",-1)),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Dimension",href:"#DimensionalData.Dimensions.Dimension"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dimension")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t(`
julia
Dimension

Abstract supertype of all dimension types.

Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.

Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.

They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.

A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.

Example:

julia
using DimensionalData, Dates
+import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framework.BAAK0EyE.js";const ss=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),d={name:"api/dimensions.md"},o={class:"jldocstring custom-block",open:""},r={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""};function $(K,s,W,J,H,Q){const n=h("Badge");return k(),p("div",null,[s[108]||(s[108]=i("h1",{id:"dimensions",tabindex:"-1"},[a("Dimensions "),i("a",{class:"header-anchor",href:"#dimensions","aria-label":'Permalink to "Dimensions"'},"​")],-1)),s[109]||(s[109]=i("p",null,[a("Dimensions are kept in the sub-module "),i("code",null,"Dimensions"),a(".")],-1)),i("details",o,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions",href:"#DimensionalData.Dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Dimensions
Sub-module for Dimensions wrappers, and operations on them used in DimensionalData.jl.
To load Dimensions types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Dimensions
source
`,5))]),s[110]||(s[110]=i("p",null,"Dimensions have a type-hierarchy that organises plotting and dimension matching.",-1)),i("details",r,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Dimension",href:"#DimensionalData.Dimensions.Dimension"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dimension")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t(`
julia
Dimension
Abstract supertype of all dimension types.
Example concrete implementations are X, Y, Z, Ti (Time), and the custom Dim dimension.
Dimensions label the axes of an AbstractDimArray, or other dimensional objects, and are used to index into an array.
They may also wrap lookup values for each array axis. This may be any AbstractVector matching the array axis length, but will usually be converted to a Lookup when use in a constructed object.
A Lookup gives more details about the dimension, such as that it is Categorical or Sampled as Points or Intervals along some transect. DimensionalData will attempt to guess the lookup from the passed-in index value.
Example:
julia
using DimensionalData, Dates
 
 x = X(2:2:10)
 y = Y(['a', 'b', 'c'])
@@ -51,21 +51,21 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
   → Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   2021-01-01T00:00:00   2021-02-01T00:00:00  …   2021-12-01T00:00:00
- 4    0.0                   0.0                      0.0
source
`,13))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.DependentDim",href:"#DimensionalData.Dimensions.DependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.DependentDim")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
',3))]),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.IndependentDim",href:"#DimensionalData.Dimensions.IndependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.IndependentDim")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
',3))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.XDim",href:"#DimensionalData.Dimensions.XDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.XDim")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t('
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
',3))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.YDim",href:"#DimensionalData.Dimensions.YDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.YDim")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
',3))]),i("details",m,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.ZDim",href:"#DimensionalData.Dimensions.ZDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.ZDim")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t('
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
',3))]),i("details",F,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.TimeDim",href:"#DimensionalData.Dimensions.TimeDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.TimeDim")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
',4))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.X",href:"#DimensionalData.Dimensions.X"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.X")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
X <: XDim
+ 4    0.0                   0.0                      0.0
source
`,13))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.DependentDim",href:"#DimensionalData.Dimensions.DependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.DependentDim")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
source
',3))]),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.IndependentDim",href:"#DimensionalData.Dimensions.IndependentDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.IndependentDim")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
source
',3))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.XDim",href:"#DimensionalData.Dimensions.XDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.XDim")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t('
julia
XDim <: IndependentDim
Abstract supertype for all X dimensions.
source
',3))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.YDim",href:"#DimensionalData.Dimensions.YDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.YDim")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
YDim <: DependentDim
Abstract supertype for all Y dimensions.
source
',3))]),i("details",m,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.ZDim",href:"#DimensionalData.Dimensions.ZDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.ZDim")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t('
julia
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
source
',3))]),i("details",F,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.TimeDim",href:"#DimensionalData.Dimensions.TimeDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.TimeDim")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
TimeDim <: IndependentDim
Abstract supertype for all time dimensions.
In a TimeDime with Interval sampling the locus will automatically be set to Start(). Dates and times generally refer to the start of a month, hour, second etc., not the central point as is more common with spatial data. `
source
',4))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.X",href:"#DimensionalData.Dimensions.X"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.X")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
X <: XDim
 
-X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
`,7))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Y",href:"#DimensionalData.Dimensions.Y"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Y")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t(`
julia
Y <: YDim
+X(val=:)
X Dimension. X <: XDim <: IndependentDim
Examples
julia
xdim = X(2:2:10)
julia
val = A[X(1)]
julia
mean(A; dims=X)
source
`,7))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Y",href:"#DimensionalData.Dimensions.Y"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Y")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t(`
julia
Y <: YDim
 
-Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
`,7))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Z",href:"#DimensionalData.Dimensions.Z"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Z")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Z <: ZDim
+Y(val=:)
Y Dimension. Y <: YDim <: DependentDim
Examples
julia
ydim = Y(['a', 'b', 'c'])
julia
val = A[Y(1)]
julia
mean(A; dims=Y)
source
`,7))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Z",href:"#DimensionalData.Dimensions.Z"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Z")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Z <: ZDim
 
-Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
`,7))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.Ti",href:"#DimensionalData.Dimensions.Ti"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Ti")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t('
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
',9))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Dim",href:"#DimensionalData.Dimensions.Dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dim")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
-custom ['a', 'b', 'c']
source
`,5))]),i("details",j,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.AnonDim",href:"#DimensionalData.Dimensions.AnonDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.AnonDim")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AnonDim <: Dimension
+Z(val=:)
Z Dimension. Z <: ZDim <: Dimension
Example:
julia
zdim = Z(10:10:100)
julia
val = A[Z(1)]
julia
mean(A; dims=Z)
source
`,7))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.Ti",href:"#DimensionalData.Dimensions.Ti"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Ti")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t('
m Ti <: TimeDim
Ti(val=:)
Time Dimension. Ti <: TimeDim <: IndependentDim
Time is already used by Dates, and T is a common type parameter, We use Ti to avoid clashes.
Example:
julia
timedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
julia
val = A[Ti(1)]
julia
mean(A; dims=Ti)
source
',9))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Dim",href:"#DimensionalData.Dimensions.Dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Dim")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
Dim{S}(val=:)
A generic dimension. For use when custom dims are required when loading data from a file. Can be used as keyword arguments for indexing.
Dimension types take precedence over same named Dim types when indexing with symbols, or e.g. creating Tables.jl keys.
julia
julia> dim = Dim{:custom}(['a', 'b', 'c'])
+custom ['a', 'b', 'c']
source
`,5))]),i("details",j,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.AnonDim",href:"#DimensionalData.Dimensions.AnonDim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.AnonDim")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AnonDim <: Dimension
 
-AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
`,3))]),i("details",B,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.@dim",href:"#DimensionalData.Dimensions.@dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.@dim")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[44]||(s[44]=t(`
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
+AnonDim()
Anonymous dimension. Used when extra dimensions are created, such as during transpose of a vector.
source
`,3))]),i("details",B,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.@dim",href:"#DimensionalData.Dimensions.@dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.@dim")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[44]||(s[44]=t(`
julia
@dim typ [supertype=Dimension] [label::String=string(typ)]
Macro to easily define new dimensions.
The supertype will be inserted into the type of the dim. The default is simply YourDim <: Dimension.
Making a Dimension inherit from XDim, YDim, ZDim or TimeDim will affect automatic plot layout and other methods that dispatch on these types. <: YDim are plotted on the Y axis, <: XDim on the X axis, etc.
label is used in plots and similar, if the dimension is short for a longer word.
Example:
julia
using DimensionalData
 using DimensionalData: @dim, YDim, XDim
 @dim Lat YDim "Latitude"
 @dim Lon XDim "Longitude"
-# output
source
`,8))]),s[111]||(s[111]=i("h3",{id:"Exported-methods",tabindex:"-1"},[a("Exported methods "),i("a",{class:"header-anchor",href:"#Exported-methods","aria-label":'Permalink to "Exported methods {#Exported-methods}"'},"​")],-1)),s[112]||(s[112]=i("p",null,"These are widely useful methods for working with dimensions.",-1)),i("details",v,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.dims-api-dimensions",href:"#DimensionalData.Dimensions.dims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+# output
source
`,8))]),s[111]||(s[111]=i("h3",{id:"Exported-methods",tabindex:"-1"},[a("Exported methods "),i("a",{class:"header-anchor",href:"#Exported-methods","aria-label":'Permalink to "Exported methods {#Exported-methods}"'},"​")],-1)),s[112]||(s[112]=i("p",null,"These are widely useful methods for working with dimensions.",-1)),i("details",v,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.dims-api-dimensions",href:"#DimensionalData.Dimensions.dims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -79,7 +79,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
`,13))]),i("details",A,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.otherdims-api-dimensions",href:"#DimensionalData.Dimensions.otherdims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+(↓ X, → Y)
source
`,13))]),i("details",A,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.otherdims-api-dimensions",href:"#DimensionalData.Dimensions.otherdims-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -87,7 +87,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
`,8))]),i("details",T,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.dimnum-api-dimensions",href:"#DimensionalData.Dimensions.dimnum-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
`,8))]),i("details",T,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.dimnum-api-dimensions",href:"#DimensionalData.Dimensions.dimnum-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -96,7 +96,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
`,8))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.hasdim-api-dimensions",href:"#DimensionalData.Dimensions.hasdim-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
`,8))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.hasdim-api-dimensions",href:"#DimensionalData.Dimensions.hasdim-api-dimensions"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -109,13 +109,13 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
`,8))]),s[113]||(s[113]=i("h3",{id:"Non-exported-methods",tabindex:"-1"},[a("Non-exported methods "),i("a",{class:"header-anchor",href:"#Non-exported-methods","aria-label":'Permalink to "Non-exported methods {#Non-exported-methods}"'},"​")],-1)),i("details",w,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.lookup",href:"#DimensionalData.Dimensions.lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.lookup")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
lookup(x::Dimension) => Lookup
+false
source
`,8))]),s[113]||(s[113]=i("h3",{id:"Non-exported-methods",tabindex:"-1"},[a("Non-exported methods "),i("a",{class:"header-anchor",href:"#Non-exported-methods","aria-label":'Permalink to "Non-exported methods {#Non-exported-methods}"'},"​")],-1)),i("details",w,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.lookup",href:"#DimensionalData.Dimensions.lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.lookup")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
lookup(x::Dimension) => Lookup
 lookup(x, [dims::Tuple]) => Tuple{Vararg{Lookup}}
 lookup(x::Tuple) => Tuple{Vararg{Lookup}}
-lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
`,5))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.label",href:"#DimensionalData.Dimensions.label"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.label")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
label(x) => String
+lookup(x, dim) => Lookup
Returns the Lookup of a dimension. This dictates properties of the dimension such as array axis and lookup order, and sampling properties.
dims can be a Dimension, a dimension type, or a tuple of either.
This is separate from val in that it will only work when dimensions actually contain an AbstractArray lookup, and can be used on a DimArray or DimStack to retrieve all lookups, as there is no ambiguity of meaning as there is with val.
source
`,5))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.label",href:"#DimensionalData.Dimensions.label"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.label")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
label(x) => String
 label(x, dims::Tuple) => NTuple{N,String}
 label(x, dim) => String
-label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",q,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.format",href:"#DimensionalData.Dimensions.format"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.format")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t('
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
',5))]),i("details",X,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.dims2indices",href:"#DimensionalData.Dimensions.dims2indices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims2indices")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
',3))]),i("details",Y,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.selectindices",href:"#DimensionalData.Dimensions.Lookups.selectindices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.selectindices")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[71]||(s[71]=t('
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
',3))]),s[114]||(s[114]=i("h3",{id:"Primitive-methods",tabindex:"-1"},[a("Primitive methods "),i("a",{class:"header-anchor",href:"#Primitive-methods","aria-label":'Permalink to "Primitive methods {#Primitive-methods}"'},"​")],-1)),s[115]||(s[115]=i("p",null,"These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.",-1)),s[116]||(s[116]=i("p",null,"They are not guaranteed to keep their interface, but usually will.",-1)),i("details",O,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.commondims",href:"#DimensionalData.Dimensions.commondims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.commondims")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=t(`
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
+label(xs::Tuple) => NTuple{N,String}
Get a plot label for data or a dimension. This will include the name and units if they exist, and anything else that should be shown on a plot.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",q,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.format",href:"#DimensionalData.Dimensions.format"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.format")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t('
julia
format(dims, x) => Tuple{Vararg{Dimension,N}}
Format the passed-in dimension(s) dims to match the object x.
Errors are thrown if dims don't match the array dims or size, and any fields holding Auto- objects are filled with guessed objects.
If a Lookup hasn't been specified, a lookup is chosen based on the type and element type of the values.
source
',5))]),i("details",X,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.dims2indices",href:"#DimensionalData.Dimensions.dims2indices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims2indices")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
dims2indices(dim::Dimension, I) => NTuple{Union{Colon,AbstractArray,Int}}
Convert a Dimension or Selector I to indices of Int, AbstractArray or Colon.
source
',3))]),i("details",Y,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.selectindices",href:"#DimensionalData.Dimensions.Lookups.selectindices"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.selectindices")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[71]||(s[71]=t('
julia
selectindices(lookups, selectors)
Converts Selector to regular indices.
source
',3))]),s[114]||(s[114]=i("h3",{id:"Primitive-methods",tabindex:"-1"},[a("Primitive methods "),i("a",{class:"header-anchor",href:"#Primitive-methods","aria-label":'Permalink to "Primitive methods {#Primitive-methods}"'},"​")],-1)),s[115]||(s[115]=i("p",null,"These low-level methods are really for internal use, but can be useful for writing dimensional algorithms.",-1)),s[116]||(s[116]=i("p",null,"They are not guaranteed to keep their interface, but usually will.",-1)),i("details",O,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.commondims",href:"#DimensionalData.Dimensions.commondims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.commondims")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=t(`
julia
commondims([f], x, query) => Tuple{Vararg{Dimension}}
This is basically dims(x, query) where the order of the original is kept, unlike dims where the query tuple determines the order
Also unlike dims,commondims always returns a Tuple, no matter the input. No errors are thrown if dims are absent from either x or query.
f is <: by default, but can be >: to sort abstract types by concrete types.
julia
julia> using DimensionalData, .Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -126,9 +126,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 (↓ X, → Z)
 
 julia> commondims(A, Ti)
-()
source
`,6))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.name2dim",href:"#DimensionalData.Dimensions.name2dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name2dim")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=t(`
julia
name2dim(s::Symbol) => Dimension
+()
source
`,6))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.name2dim",href:"#DimensionalData.Dimensions.name2dim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name2dim")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=t(`
julia
name2dim(s::Symbol) => Dimension
 name2dim(dims...) => Tuple{Dimension,Vararg}
-name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
`,4))]),i("details",S,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.reducedims",href:"#DimensionalData.Dimensions.reducedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.reducedims")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[80]||(s[80]=t('
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
',4))]),i("details",Z,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.swapdims",href:"#DimensionalData.Dimensions.swapdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.swapdims")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=t(`
julia
swapdims(x::T, newdims) => T
+name2dim(dims::Tuple) => Tuple{Dimension,Vararg}
Convert a symbol to a dimension object. :X, :Y, :Ti etc will be converted to X(), Y(), Ti(), as with any other dims generated with the @dim macro.
All other Symbols S will generate Dim{S}() dimensions.
source
`,4))]),i("details",S,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.reducedims",href:"#DimensionalData.Dimensions.reducedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.reducedims")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[80]||(s[80]=t('
julia
reducedims(x, dimstoreduce) => Tuple{Vararg{Dimension}}
Replace the specified dimensions with an index of length 1. This is usually to match a new array size where an axis has been reduced with a method like mean or reduce to a length of 1, but the number of dimensions has not changed.
Lookup traits are also updated to correspond to the change in cell step, sampling type and order.
source
',4))]),i("details",Z,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.swapdims",href:"#DimensionalData.Dimensions.swapdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.swapdims")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=t(`
julia
swapdims(x::T, newdims) => T
 swapdims(dims::Tuple, newdims) => Tuple{Vararg{Dimension}}
Swap dimensions for the passed in dimensions, in the order passed.
Passing in the Dimension types rewraps the dimension index, keeping the index values and metadata, while constructed Dimension objects replace the original dimension. nothing leaves the original dimension as-is.
Arguments
  • x: any object with a dims method or a Tuple of Dimension.
  • newdim: Tuple of Dimension or dimension Type.
Example
julia
using DimensionalData
 A = ones(X(2), Y(4), Z(2))
 Dimensions.swapdims(A, (Dim{:a}, Dim{:b}, Dim{:c}))
@@ -141,17 +141,17 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as h,o as k}from"./chunks/framewo
 └───────────────────────────┘
 [:, :, 1]
  1.0  1.0  1.0  1.0
- 1.0  1.0  1.0  1.0
source
`,8))]),i("details",I,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.slicedims",href:"#DimensionalData.Dimensions.slicedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.slicedims")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=t(`
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
-slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
`,7))]),i("details",N,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.comparedims",href:"#DimensionalData.Dimensions.comparedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.comparedims")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=t(`
julia
comparedims(A::AbstractDimArray...; kw...)
+ 1.0  1.0  1.0  1.0
source
`,8))]),i("details",I,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.slicedims",href:"#DimensionalData.Dimensions.slicedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.slicedims")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=t(`
julia
slicedims(x, I) => Tuple{Tuple,Tuple}
+slicedims(f, x, I) => Tuple{Tuple,Tuple}
Slice the dimensions to match the axis values of the new array.
All methods return a tuple containing two tuples: the new dimensions, and the reference dimensions. The ref dimensions are no longer used in the new struct but are useful to give context to plots.
Called at the array level the returned tuple will also include the previous reference dims attached to the array.
Arguments
  • f: a function getindex, view or dotview. This will be used for slicing getindex is the default if f is not included.
  • x: An AbstractDimArray, Tuple of Dimension, or Dimension
  • I: A tuple of Integer, Colon or AbstractArray
source
`,7))]),i("details",N,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.comparedims",href:"#DimensionalData.Dimensions.comparedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.comparedims")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=t(`
julia
comparedims(A::AbstractDimArray...; kw...)
 comparedims(A::Tuple...; kw...)
 comparedims(A::Dimension...; kw...)
-comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
`,8))]),i("details",P,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.combinedims",href:"#DimensionalData.Dimensions.combinedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.combinedims")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=t('
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
',4))]),i("details",V,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.sortdims",href:"#DimensionalData.Dimensions.sortdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.sortdims")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=t('
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
',5))]),i("details",M,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.basetypeof",href:"#DimensionalData.Dimensions.Lookups.basetypeof"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.basetypeof")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=t('
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
',4))]),i("details",R,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.basedims",href:"#DimensionalData.Dimensions.basedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.basedims")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[101]||(s[101]=t(`
julia
basedims(ds::Tuple)
-basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
`,4))]),i("details",G,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.setdims",href:"#DimensionalData.Dimensions.setdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.setdims")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=t(`
julia
setdims(X, newdims) => AbstractArray
+comparedims(::Type{Bool}, args...; kw...)
Check that dimensions or tuples of dimensions passed as each argument are the same, and return the first valid dimension. If AbstractDimArrays are passed as arguments their dimensions are compared.
Empty tuples and nothing dimension values are ignored, returning the Dimension value if it exists.
Passing Bool as the first argument means true/false will be returned, rather than throwing an error.
Keywords
These are all Bool flags:
  • type: compare dimension type, true by default.
  • valtype: compare wrapped value type, false by default.
  • val: compare wrapped values, false by default.
  • order: compare order, false by default.
  • length: compare lengths, true by default.
  • ignore_length_one: ignore length 1 in comparisons, and return whichever dimension is not length 1, if any. This is useful in e.g. broadcasting comparisons. false by default.
  • msg: DimensionalData.Warn or DimensionalData.Throw. Both may contain string, which will be added to error or warning mesages.
source
`,8))]),i("details",P,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.combinedims",href:"#DimensionalData.Dimensions.combinedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.combinedims")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=t('
julia
combinedims(xs; check=true, kw...)
Combine the dimensions of each object in xs, in the order they are found.
Keywords are passed to comparedims.
source
',4))]),i("details",V,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.sortdims",href:"#DimensionalData.Dimensions.sortdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.sortdims")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=t('
julia
sortdims([f], tosort, order) => Tuple
Sort dimensions tosort by order. Dimensions in order but missing from tosort are replaced with nothing.
tosort and order can be Tuples or Vectors or Dimension or dimension type. Abstract supertypes like TimeDim can be used in order.
f is <: by default, but can be >: to sort abstract types by concrete types.
source
',5))]),i("details",M,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.basetypeof",href:"#DimensionalData.Dimensions.Lookups.basetypeof"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.basetypeof")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=t('
julia
basetypeof(x) => Type
Get the "base" type of an object - the minimum required to define the object without it's fields. By default this is the full UnionAll for the type. But custom basetypeof methods can be defined for types with free type parameters.
In DimensionalData this is primarily used for comparing Dimensions, where Dim{:x} is different from Dim{:y}.
source
',4))]),i("details",R,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.basedims",href:"#DimensionalData.Dimensions.basedims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.basedims")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[101]||(s[101]=t(`
julia
basedims(ds::Tuple)
+basedims(d::Union{Dimension,Symbol,Type})
Returns basetypeof(d)() or a Tuple of called on a Tuple.
See basetypeof
source
`,4))]),i("details",G,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.setdims",href:"#DimensionalData.Dimensions.setdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.setdims")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=t(`
julia
setdims(X, newdims) => AbstractArray
 setdims(::Tuple, newdims) => Tuple{Vararg{Dimension,N}}
Replaces the first dim matching <: basetypeof(newdim) with newdim, and returns a new object or tuple with the dimension updated.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • newdim: Tuple or single Dimension, Type or Symbol.
Example
julia
using DimensionalData, DimensionalData.Dimensions, DimensionalData.Lookups
 A = ones(X(10), Y(10:10:100))
 B = setdims(A, Y(Categorical('a':'j'; order=ForwardOrdered())))
 lookup(B, Y)
 # output
 Categorical{Char} ForwardOrdered
-wrapping: 'a':1:'j'
source
`,7))]),i("details",U,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.dimsmatch",href:"#DimensionalData.Dimensions.dimsmatch"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimsmatch")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=t(`
julia
dimsmatch([f], dim, query) => Bool
-dimsmatch([f], dims::Tuple, query::Tuple) => Bool
Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.
f is <: by default, but can be >: to match abstract types to concrete types.
source
`,4))])])}const is=l(d,[["render",$]]);export{ss as __pageData,is as default};
+wrapping: 'a':1:'j'

source

`,7))]),i("details",U,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.dimsmatch",href:"#DimensionalData.Dimensions.dimsmatch"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimsmatch")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=t(`
julia
dimsmatch([f], dim, query) => Bool
+dimsmatch([f], dims::Tuple, query::Tuple) => Bool

Compare 2 dimensions or Tuple of Dimension are of the same base type, or are at least rotations/transformations of the same type.

f is <: by default, but can be >: to match abstract types to concrete types.

source

`,4))])])}const is=l(d,[["render",$]]);export{ss as __pageData,is as default}; diff --git a/dev/assets/api_lookuparrays.md.4gWyIReH.js b/dev/assets/api_lookuparrays.md.QQHTyv_S.js similarity index 96% rename from dev/assets/api_lookuparrays.md.4gWyIReH.js rename to dev/assets/api_lookuparrays.md.QQHTyv_S.js index 3453e5208..7e142badc 100644 --- a/dev/assets/api_lookuparrays.md.4gWyIReH.js +++ b/dev/assets/api_lookuparrays.md.QQHTyv_S.js @@ -1,5 +1,5 @@ -import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framework.B8l2DGq3.js";const bs=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),k={name:"api/lookuparrays.md"},r={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},os={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},gs={class:"jldocstring custom-block",open:""},cs={class:"jldocstring custom-block",open:""};function us(ys,s,Es,ms,Ds,Fs){const n=o("Badge");return h(),p("div",null,[s[174]||(s[174]=i("h1",{id:"lookups",tabindex:"-1"},[a("Lookups "),i("a",{class:"header-anchor",href:"#lookups","aria-label":'Permalink to "Lookups"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions.Lookups",href:"#DimensionalData.Dimensions.Lookups"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Lookups

Module for Lookups and Selectors used in DimensionalData.jl

Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.

For example, these allow tracking over array order so fast indexing works even when the array is reversed.

To load Lookup types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Lookups

source

`,7))]),i("details",d,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Lookups.Lookup",href:"#DimensionalData.Dimensions.Lookups.Lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Lookup")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t('
julia
Lookup

Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.

A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.

source

',4))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.Lookups.Aligned",href:"#DimensionalData.Dimensions.Lookups.Aligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Aligned")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
Aligned <: Lookup

Abstract supertype for Lookups where the lookup is aligned with the array axes.

This is by far the most common supertype for Lookup.

source

',4))]),i("details",c,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractSampled",href:"#DimensionalData.Dimensions.Lookups.AbstractSampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractSampled")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
AbstractSampled <: Aligned

Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.

AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.

source

',4))]),i("details",u,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampled",href:"#DimensionalData.Dimensions.Lookups.Sampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampled")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t(`
julia
Sampled <: AbstractSampled
+import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framework.BAAK0EyE.js";const bs=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),k={name:"api/lookuparrays.md"},r={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},os={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},gs={class:"jldocstring custom-block",open:""},cs={class:"jldocstring custom-block",open:""};function us(ys,s,Es,ms,Ds,Fs){const n=o("Badge");return h(),p("div",null,[s[174]||(s[174]=i("h1",{id:"lookups",tabindex:"-1"},[a("Lookups "),i("a",{class:"header-anchor",href:"#lookups","aria-label":'Permalink to "Lookups"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions.Lookups",href:"#DimensionalData.Dimensions.Lookups"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Lookups
Module for Lookups and Selectors used in DimensionalData.jl
Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.
For example, these allow tracking over array order so fast indexing works even when the array is reversed.
To load Lookup types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Lookups
source
`,7))]),i("details",d,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Lookups.Lookup",href:"#DimensionalData.Dimensions.Lookups.Lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Lookup")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t('
julia
Lookup
Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.
A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.
source
',4))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.Lookups.Aligned",href:"#DimensionalData.Dimensions.Lookups.Aligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Aligned")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
Aligned <: Lookup
Abstract supertype for Lookups where the lookup is aligned with the array axes.
This is by far the most common supertype for Lookup.
source
',4))]),i("details",c,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractSampled",href:"#DimensionalData.Dimensions.Lookups.AbstractSampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractSampled")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
AbstractSampled <: Aligned
Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.
AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.
source
',4))]),i("details",u,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampled",href:"#DimensionalData.Dimensions.Lookups.Sampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampled")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t(`
julia
Sampled <: AbstractSampled
 
 Sampled(data::AbstractVector, order::Order, span::Span, sampling::Sampling, metadata)
 Sampled(data=AutoValues(); order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata())
A concrete implementation of the Lookup AbstractSampled. It can be used to represent Points or Intervals.
Sampled is capable of representing gridded data from a wide range of sources, allowing correct bounds and Selectors for points or intervals of regular, irregular, forward and reverse lookups.
On AbstractDimArray construction, Sampled lookup is assigned for all lookups of AbstractRange not assigned to Categorical.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with Interval sampling, and Regular span for a vector with known spacing.
We set the locus of the Intervals to Start specifying that the lookup values are for the locus at the start of each interval.
julia
using DimensionalData, DimensionalData.Lookups
@@ -20,9 +20,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   80    1.0  1.0  1.0   1.0
   60    1.0  1.0  1.0   1.0
   40    1.0  1.0  1.0   1.0
-  20    1.0  1.0  1.0   1.0
source
`,11))]),i("details",y,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCyclic",href:"#DimensionalData.Dimensions.Lookups.AbstractCyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCyclic")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
',4))]),i("details",E,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.Lookups.Cyclic",href:"#DimensionalData.Dimensions.Lookups.Cyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Cyclic")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t(`
julia
Cyclic <: AbstractCyclic
+  20    1.0  1.0  1.0   1.0
source
`,11))]),i("details",y,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCyclic",href:"#DimensionalData.Dimensions.Lookups.AbstractCyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCyclic")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
',4))]),i("details",E,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.Lookups.Cyclic",href:"#DimensionalData.Dimensions.Lookups.Cyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Cyclic")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t(`
julia
Cyclic <: AbstractCyclic
 
-Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
`,8))]),i("details",m,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCategorical",href:"#DimensionalData.Dimensions.Lookups.AbstractCategorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCategorical")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
',5))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.Lookups.Categorical",href:"#DimensionalData.Dimensions.Lookups.Categorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Categorical")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
Categorical <: AbstractCategorical
+Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
`,8))]),i("details",m,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCategorical",href:"#DimensionalData.Dimensions.Lookups.AbstractCategorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCategorical")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
',5))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.Lookups.Categorical",href:"#DimensionalData.Dimensions.Lookups.Categorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Categorical")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
Categorical <: AbstractCategorical
 
 Categorical(o::Order)
 Categorical(; order=Unordered())
A Lookup where the values are categories.
This will be automatically assigned if the lookup contains AbstractString, Symbol or Char. Otherwise it can be assigned manually.
Order will be determined automatically where possible.
Arguments
  • data: An AbstractVector matching the length of the corresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. Can be provided if this is known and performance is important.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with [Interval] sampling.
julia
using DimensionalData
@@ -34,7 +34,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 # output
 
 Categorical{String} ["one", "two", "three"] Unordered,
-Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
`,10))]),i("details",F,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unaligned",href:"#DimensionalData.Dimensions.Lookups.Unaligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unaligned")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t('
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
',4))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Lookups.Transformed",href:"#DimensionalData.Dimensions.Lookups.Transformed"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Transformed")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Transformed <: Unaligned
+Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
`,10))]),i("details",F,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unaligned",href:"#DimensionalData.Dimensions.Lookups.Unaligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unaligned")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t('
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
',4))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Lookups.Transformed",href:"#DimensionalData.Dimensions.Lookups.Transformed"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Transformed")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Transformed <: Unaligned
 
 Transformed(f, dim::Dimension; metadata=NoMetadata())
Lookup that uses an affine transformation to convert dimensions from dims(lookup) to dims(array). This can be useful when the dimensions are e.g. rotated from a more commonly used axis.
Any function can be used to do the transformation, but transformations from CoordinateTransformations.jl may be useful.
Arguments
  • f: transformation function
  • dim: a dimension to transform to.
Keyword Arguments
  • metadata:
Example
julia
using DimensionalData, DimensionalData.Lookups, CoordinateTransformations
 
@@ -47,9 +47,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 da[X(At(6.0)), Y(At(2.0))]
 
 # output
-9
source
`,10))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.MergedLookup",href:"#DimensionalData.Dimensions.MergedLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.MergedLookup")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t(`
julia
MergedLookup <: Lookup
+9
source
`,10))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.MergedLookup",href:"#DimensionalData.Dimensions.MergedLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.MergedLookup")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t(`
julia
MergedLookup <: Lookup
 
-MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
`,8))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoLookup",href:"#DimensionalData.Dimensions.Lookups.NoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoLookup")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
NoLookup <: Lookup
+MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
`,8))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoLookup",href:"#DimensionalData.Dimensions.Lookups.NoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoLookup")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
NoLookup <: Lookup
 
 NoLookup()
A Lookup that is identical to the array axis. Selectors can't be used on this lookup.
Example
Defining a DimArray without passing lookup values to the dimensions, it will be assigned NoLookup:
julia
using DimensionalData
 
@@ -64,26 +64,26 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 
 # output
 
-NoLookup, NoLookup
source
`,8))]),i("details",v,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoLookup",href:"#DimensionalData.Dimensions.Lookups.AutoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoLookup")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AutoLookup <: Lookup
+NoLookup, NoLookup
source
`,8))]),i("details",v,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoLookup",href:"#DimensionalData.Dimensions.Lookups.AutoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoLookup")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AutoLookup <: Lookup
 
 AutoLookup()
-AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
`,4))]),i("details",j,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoValues",href:"#DimensionalData.Dimensions.Lookups.AutoValues"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoValues")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=t('
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
',3))]),s[175]||(s[175]=i("p",null,[a("The generic value getter "),i("code",null,"val")],-1)),i("details",B,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.Lookups.val",href:"#DimensionalData.Dimensions.Lookups.val"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.val")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
val(x)
-val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
`,5))]),s[176]||(s[176]=i("p",null,"Lookup methods:",-1)),i("details",L,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.Lookups.bounds",href:"#DimensionalData.Dimensions.Lookups.bounds"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.bounds")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
+AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
`,4))]),i("details",j,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoValues",href:"#DimensionalData.Dimensions.Lookups.AutoValues"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoValues")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=t('
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
',3))]),s[175]||(s[175]=i("p",null,[a("The generic value getter "),i("code",null,"val")],-1)),i("details",B,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.Lookups.val",href:"#DimensionalData.Dimensions.Lookups.val"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.val")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
val(x)
+val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
`,5))]),s[176]||(s[176]=i("p",null,"Lookup methods:",-1)),i("details",L,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.Lookups.bounds",href:"#DimensionalData.Dimensions.Lookups.bounds"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.bounds")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
 bounds(xs::Tuple) => Tuple{Vararg{Tuple{T,T}}}
 bounds(x, dim) => Tuple{T,T}
-bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
`,5))]),i("details",A,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.Lookups.hasselection",href:"#DimensionalData.Dimensions.Lookups.hasselection"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.hasselection")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
hasselection(x, selector) => Bool
-hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
`,3))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.Lookups.sampling",href:"#DimensionalData.Dimensions.Lookups.sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.sampling")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
sampling(x, [dims::Tuple]) => Tuple
+bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
`,5))]),i("details",A,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.Lookups.hasselection",href:"#DimensionalData.Dimensions.Lookups.hasselection"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.hasselection")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
hasselection(x, selector) => Bool
+hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
`,3))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.Lookups.sampling",href:"#DimensionalData.Dimensions.Lookups.sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.sampling")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
sampling(x, [dims::Tuple]) => Tuple
 sampling(x, dim) => Sampling
 sampling(xs::Tuple) => Tuple{Vararg{Sampling}}
-sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.Lookups.span",href:"#DimensionalData.Dimensions.Lookups.span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.span")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
span(x, [dims::Tuple]) => Tuple
+sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.Lookups.span",href:"#DimensionalData.Dimensions.Lookups.span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.span")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
span(x, [dims::Tuple]) => Tuple
 span(x, dim) => Span
 span(xs::Tuple) => Tuple{Vararg{Span,N}}
-span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",w,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.Lookups.order",href:"#DimensionalData.Dimensions.Lookups.order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.order")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
order(x, [dims::Tuple]) => Tuple
+span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",w,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.Lookups.order",href:"#DimensionalData.Dimensions.Lookups.order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.order")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
order(x, [dims::Tuple]) => Tuple
 order(xs::Tuple) => Tuple
-order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.Lookups.locus",href:"#DimensionalData.Dimensions.Lookups.locus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.locus")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t(`
julia
locus(x, [dims::Tuple]) => Tuple
+order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.Lookups.locus",href:"#DimensionalData.Dimensions.Lookups.locus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.locus")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t(`
julia
locus(x, [dims::Tuple]) => Tuple
 locus(x, dim) => Locus
 locus(xs::Tuple) => Tuple{Vararg{Locus,N}}
-locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",O,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.Lookups.shiftlocus",href:"#DimensionalData.Dimensions.Lookups.shiftlocus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.shiftlocus")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
',4))]),s[177]||(s[177]=i("h2",{id:"selectors",tabindex:"-1"},[a("Selectors "),i("a",{class:"header-anchor",href:"#selectors","aria-label":'Permalink to "Selectors"'},"​")],-1)),i("details",I,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.Selector",href:"#DimensionalData.Dimensions.Lookups.Selector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Selector")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=t('
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
',10))]),i("details",q,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.Lookups.IntSelector",href:"#DimensionalData.Dimensions.Lookups.IntSelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.IntSelector")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[74]||(s[74]=t('
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
',5))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.Lookups.ArraySelector",href:"#DimensionalData.Dimensions.Lookups.ArraySelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ArraySelector")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[77]||(s[77]=t('
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
',5))]),i("details",P,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.Lookups.At",href:"#DimensionalData.Dimensions.Lookups.At"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.At")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=t(`
julia
At <: IntSelector
+locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",O,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.Lookups.shiftlocus",href:"#DimensionalData.Dimensions.Lookups.shiftlocus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.shiftlocus")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
',4))]),s[177]||(s[177]=i("h2",{id:"selectors",tabindex:"-1"},[a("Selectors "),i("a",{class:"header-anchor",href:"#selectors","aria-label":'Permalink to "Selectors"'},"​")],-1)),i("details",I,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.Selector",href:"#DimensionalData.Dimensions.Lookups.Selector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Selector")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=t('
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
',10))]),i("details",q,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.Lookups.IntSelector",href:"#DimensionalData.Dimensions.Lookups.IntSelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.IntSelector")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[74]||(s[74]=t('
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
',5))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.Lookups.ArraySelector",href:"#DimensionalData.Dimensions.Lookups.ArraySelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ArraySelector")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[77]||(s[77]=t('
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
',5))]),i("details",P,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.Lookups.At",href:"#DimensionalData.Dimensions.Lookups.At"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.At")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=t(`
julia
At <: IntSelector
 
 At(x; atol=nothing, rtol=nothing)
 At(a, b; kw...)
Selector that exactly matches the value on the passed-in dimensions, or throws an error. For ranges and arrays, every intermediate value must match an existing value - not just the end points.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Keyword atol is passed to isapprox.
Example
julia
using DimensionalData
@@ -93,7 +93,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 
 # output
 
-5
source
`,7))]),i("details",N,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.Lookups.Near",href:"#DimensionalData.Dimensions.Lookups.Near"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Near")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[83]||(s[83]=t(`
julia
Near <: IntSelector
+5
source
`,7))]),i("details",N,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.Lookups.Near",href:"#DimensionalData.Dimensions.Lookups.Near"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Near")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[83]||(s[83]=t(`
julia
Near <: IntSelector
 
 Near(x)
 Near(a, b)
Selector that selects the nearest index to x.
With Points this is simply the lookup values nearest to the x, however with Intervals it is the interval center nearest to x. This will be offset from the index value for Start and End locus.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between the nearsest value to each of them will be selected.
Example
julia
using DimensionalData
@@ -102,7 +102,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 A[X(Near(23)), Y(Near(5.1))]
 
 # output
-4
source
`,7))]),i("details",R,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.Lookups.Between",href:"#DimensionalData.Dimensions.Lookups.Between"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Between")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[86]||(s[86]=t(`
julia
Between <: ArraySelector
+4
source
`,7))]),i("details",R,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.Lookups.Between",href:"#DimensionalData.Dimensions.Lookups.Between"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Between")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[86]||(s[86]=t(`
julia
Between <: ArraySelector
 
 Between(a, b)
Depreciated: use a..b instead of Between(a, b). Other Interval objects from IntervalSets.jl, like \`OpenInterval(a, b) will also work, giving the correct open/closed boundaries.
Between will e removed in future to avoid clashes with DataFrames.Between.
Selector that retrieve all indices located between 2 values, evaluated with >= for the lower value, and < for the upper value. This means the same value will not be counted twice in 2 adjacent Between selections.
For Intervals the whole interval must be lie between the values. For Points the points must fall between the values. Different Sampling types may give different results with the same input - this is the intended behaviour.
Between for Irregular intervals is a little complicated. The interval is the distance between a value and the next (for Start locus) or previous (for End locus) value.
For Center, we take the mid point between two index values as the start and end of each interval. This may or may not make sense for the values in your index, so use Between with Irregular Intervals(Center()) with caution.
Example
julia
using DimensionalData
 
@@ -118,7 +118,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
`,10))]),i("details",M,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.Lookups.Touches",href:"#DimensionalData.Dimensions.Lookups.Touches"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Touches")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[89]||(s[89]=t(`
julia
Touches <: ArraySelector
+ 20    4  5
source
`,10))]),i("details",M,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.Lookups.Touches",href:"#DimensionalData.Dimensions.Lookups.Touches"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Touches")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[89]||(s[89]=t(`
julia
Touches <: ArraySelector
 
 Touches(a, b)
Selector that retrieves all indices touching the closed interval 2 values, for the maximum possible area that could interact with the supplied range.
This can be better than .. when e.g. subsetting an area to rasterize, as you may wish to include pixels that just touch the area, rather than those that fall within it.
Touches is different to using closed intervals when the lookups also contain intervals - if any of the intervals touch, they are included. With .. they are discarded unless the whole cell interval falls inside the selector interval.
Example
julia
using DimensionalData
 
@@ -133,7 +133,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
`,7))]),i("details",U,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.Contains",href:"#DimensionalData.Dimensions.Lookups.Contains"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Contains")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=t(`
julia
Contains <: IntSelector
+ 20    4  5
source
`,7))]),i("details",U,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.Contains",href:"#DimensionalData.Dimensions.Lookups.Contains"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Contains")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=t(`
julia
Contains <: IntSelector
 
 Contains(x)
 Contains(a, b)
Selector that selects the interval the value is contained by. If the interval is not present in the lookup, an error will be thrown.
Can only be used for Intervals or Categorical. For Categorical it falls back to using At. Contains should not be confused with Base.contains - use Where(contains(x)) to check for if values are contain in categorical values like strings.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Example
julia
using DimensionalData; const DD = DimensionalData
@@ -143,7 +143,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 A[X(Contains(8)), Y(Contains(6.8))]
 
 # output
-3
source
`,7))]),i("details",X,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.Where",href:"#DimensionalData.Dimensions.Lookups.Where"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Where")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=t(`
julia
Where <: ArraySelector
+3
source
`,7))]),i("details",X,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.Where",href:"#DimensionalData.Dimensions.Lookups.Where"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Where")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=t(`
julia
Where <: ArraySelector
 
 Where(f::Function)
Selector that filters a dimension lookup by any function that accepts a single value and returns a Bool.
Example
julia
using DimensionalData
 
@@ -159,7 +159,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
 └─────────────────────────────────────────────────────────────┘
   ↓ →  19  21
- 20     4   6
source
`,5))]),i("details",Y,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.All",href:"#DimensionalData.Dimensions.Lookups.All"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.All")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[98]||(s[98]=t(`
julia
All <: Selector
+ 20     4   6
source
`,5))]),i("details",Y,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.All",href:"#DimensionalData.Dimensions.Lookups.All"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.All")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[98]||(s[98]=t(`
julia
All <: Selector
 
 All(selectors::Selector...)
Selector that combines the results of other selectors. The indices used will be the union of all result sorted in ascending order.
Example
julia
using DimensionalData, Unitful
 
@@ -177,42 +177,42 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 └──────────────────────────────────────────────────────────────────────────────┘
   ↓ →  1 s  6 s  91 s  96 s
  10.0    1    2    19    20
- 50.0    3    6    57    60
source
`,5))]),s[178]||(s[178]=i("h2",{id:"Lookup-traits",tabindex:"-1"},[a("Lookup traits "),i("a",{class:"header-anchor",href:"#Lookup-traits","aria-label":'Permalink to "Lookup traits {#Lookup-traits}"'},"​")],-1)),i("details",V,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.Lookups.LookupTrait",href:"#DimensionalData.Dimensions.Lookups.LookupTrait"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.LookupTrait")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=t('
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
',5))]),s[179]||(s[179]=i("h3",{id:"order",tabindex:"-1"},[a("Order "),i("a",{class:"header-anchor",href:"#order","aria-label":'Permalink to "Order"'},"​")],-1)),i("details",W,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.Lookups.Order",href:"#DimensionalData.Dimensions.Lookups.Order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Order")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=t('
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
',3))]),i("details",G,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.Lookups.Ordered",href:"#DimensionalData.Dimensions.Lookups.Ordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Ordered")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=t('
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
',3))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.Dimensions.Lookups.ForwardOrdered",href:"#DimensionalData.Dimensions.Lookups.ForwardOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ForwardOrdered")],-1)),s[109]||(s[109]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=t(`
julia
ForwardOrdered <: Ordered
+ 50.0    3    6    57    60
source
`,5))]),s[178]||(s[178]=i("h2",{id:"Lookup-traits",tabindex:"-1"},[a("Lookup traits "),i("a",{class:"header-anchor",href:"#Lookup-traits","aria-label":'Permalink to "Lookup traits {#Lookup-traits}"'},"​")],-1)),i("details",V,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.Lookups.LookupTrait",href:"#DimensionalData.Dimensions.Lookups.LookupTrait"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.LookupTrait")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=t('
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
',5))]),s[179]||(s[179]=i("h3",{id:"order",tabindex:"-1"},[a("Order "),i("a",{class:"header-anchor",href:"#order","aria-label":'Permalink to "Order"'},"​")],-1)),i("details",W,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.Lookups.Order",href:"#DimensionalData.Dimensions.Lookups.Order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Order")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=t('
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
',3))]),i("details",G,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.Lookups.Ordered",href:"#DimensionalData.Dimensions.Lookups.Ordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Ordered")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=t('
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
',3))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.Dimensions.Lookups.ForwardOrdered",href:"#DimensionalData.Dimensions.Lookups.ForwardOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ForwardOrdered")],-1)),s[109]||(s[109]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=t(`
julia
ForwardOrdered <: Ordered
 
-ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
`,3))]),i("details",$,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.Dimensions.Lookups.ReverseOrdered",href:"#DimensionalData.Dimensions.Lookups.ReverseOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ReverseOrdered")],-1)),s[112]||(s[112]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=t(`
julia
ReverseOrdered <: Ordered
+ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
`,3))]),i("details",$,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.Dimensions.Lookups.ReverseOrdered",href:"#DimensionalData.Dimensions.Lookups.ReverseOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ReverseOrdered")],-1)),s[112]||(s[112]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=t(`
julia
ReverseOrdered <: Ordered
 
-ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
`,3))]),i("details",H,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unordered",href:"#DimensionalData.Dimensions.Lookups.Unordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unordered")],-1)),s[115]||(s[115]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=t(`
julia
Unordered <: Order
+ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
`,3))]),i("details",H,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unordered",href:"#DimensionalData.Dimensions.Lookups.Unordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unordered")],-1)),s[115]||(s[115]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=t(`
julia
Unordered <: Order
 
-Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
`,4))]),i("details",J,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoOrder",href:"#DimensionalData.Dimensions.Lookups.AutoOrder"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoOrder")],-1)),s[118]||(s[118]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=t(`
julia
AutoOrder <: Order
+Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
`,4))]),i("details",J,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoOrder",href:"#DimensionalData.Dimensions.Lookups.AutoOrder"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoOrder")],-1)),s[118]||(s[118]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=t(`
julia
AutoOrder <: Order
 
-AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
`,3))]),s[180]||(s[180]=i("h3",{id:"span",tabindex:"-1"},[a("Span "),i("a",{class:"header-anchor",href:"#span","aria-label":'Permalink to "Span"'},"​")],-1)),i("details",Q,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.Dimensions.Lookups.Span",href:"#DimensionalData.Dimensions.Lookups.Span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Span")],-1)),s[121]||(s[121]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=t('
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
',3))]),i("details",Z,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.Dimensions.Lookups.Regular",href:"#DimensionalData.Dimensions.Lookups.Regular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Regular")],-1)),s[124]||(s[124]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=t(`
julia
Regular <: Span
+AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
`,3))]),s[180]||(s[180]=i("h3",{id:"span",tabindex:"-1"},[a("Span "),i("a",{class:"header-anchor",href:"#span","aria-label":'Permalink to "Span"'},"​")],-1)),i("details",Q,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.Dimensions.Lookups.Span",href:"#DimensionalData.Dimensions.Lookups.Span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Span")],-1)),s[121]||(s[121]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=t('
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
',3))]),i("details",Z,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.Dimensions.Lookups.Regular",href:"#DimensionalData.Dimensions.Lookups.Regular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Regular")],-1)),s[124]||(s[124]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=t(`
julia
Regular <: Span
 
-Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
`,3))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.Dimensions.Lookups.Irregular",href:"#DimensionalData.Dimensions.Lookups.Irregular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Irregular")],-1)),s[127]||(s[127]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[128]||(s[128]=t(`
julia
Irregular <: Span
+Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
`,3))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.Dimensions.Lookups.Irregular",href:"#DimensionalData.Dimensions.Lookups.Irregular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Irregular")],-1)),s[127]||(s[127]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[128]||(s[128]=t(`
julia
Irregular <: Span
 
 Irregular(bounds::Tuple)
-Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
`,3))]),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"DimensionalData.Dimensions.Lookups.Explicit",href:"#DimensionalData.Dimensions.Lookups.Explicit"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Explicit")],-1)),s[130]||(s[130]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[131]||(s[131]=t('
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
',4))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoSpan",href:"#DimensionalData.Dimensions.Lookups.AutoSpan"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoSpan")],-1)),s[133]||(s[133]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[134]||(s[134]=t(`
julia
AutoSpan <: Span
+Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
`,3))]),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"DimensionalData.Dimensions.Lookups.Explicit",href:"#DimensionalData.Dimensions.Lookups.Explicit"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Explicit")],-1)),s[130]||(s[130]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[131]||(s[131]=t('
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
',4))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoSpan",href:"#DimensionalData.Dimensions.Lookups.AutoSpan"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoSpan")],-1)),s[133]||(s[133]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[134]||(s[134]=t(`
julia
AutoSpan <: Span
 
-AutoSpan()
The span will be guessed and replaced in format or set.
source
`,3))]),s[181]||(s[181]=i("h3",{id:"sampling",tabindex:"-1"},[a("Sampling "),i("a",{class:"header-anchor",href:"#sampling","aria-label":'Permalink to "Sampling"'},"​")],-1)),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampling",href:"#DimensionalData.Dimensions.Lookups.Sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampling")],-1)),s[136]||(s[136]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[137]||(s[137]=t('
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
',3))]),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.Dimensions.Lookups.Points",href:"#DimensionalData.Dimensions.Lookups.Points"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Points")],-1)),s[139]||(s[139]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=t(`
julia
Points <: Sampling
+AutoSpan()
The span will be guessed and replaced in format or set.
source
`,3))]),s[181]||(s[181]=i("h3",{id:"sampling",tabindex:"-1"},[a("Sampling "),i("a",{class:"header-anchor",href:"#sampling","aria-label":'Permalink to "Sampling"'},"​")],-1)),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampling",href:"#DimensionalData.Dimensions.Lookups.Sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampling")],-1)),s[136]||(s[136]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[137]||(s[137]=t('
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
',3))]),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.Dimensions.Lookups.Points",href:"#DimensionalData.Dimensions.Lookups.Points"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Points")],-1)),s[139]||(s[139]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=t(`
julia
Points <: Sampling
 
-Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
`,4))]),i("details",es,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Dimensions.Lookups.Intervals",href:"#DimensionalData.Dimensions.Lookups.Intervals"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Intervals")],-1)),s[142]||(s[142]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=t(`
julia
Intervals <: Sampling
+Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
`,4))]),i("details",es,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Dimensions.Lookups.Intervals",href:"#DimensionalData.Dimensions.Lookups.Intervals"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Intervals")],-1)),s[142]||(s[142]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=t(`
julia
Intervals <: Sampling
 
-Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
`,4))]),s[182]||(s[182]=i("h3",{id:"positions",tabindex:"-1"},[a("Positions "),i("a",{class:"header-anchor",href:"#positions","aria-label":'Permalink to "Positions"'},"​")],-1)),i("details",ts,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.Dimensions.Lookups.Position",href:"#DimensionalData.Dimensions.Lookups.Position"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Position")],-1)),s[145]||(s[145]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=t('
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
',5))]),i("details",ls,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.Dimensions.Lookups.Center",href:"#DimensionalData.Dimensions.Lookups.Center"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Center")],-1)),s[148]||(s[148]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=t(`
julia
Center <: Position
+Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
`,4))]),s[182]||(s[182]=i("h3",{id:"positions",tabindex:"-1"},[a("Positions "),i("a",{class:"header-anchor",href:"#positions","aria-label":'Permalink to "Positions"'},"​")],-1)),i("details",ts,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.Dimensions.Lookups.Position",href:"#DimensionalData.Dimensions.Lookups.Position"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Position")],-1)),s[145]||(s[145]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=t('
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
',5))]),i("details",ls,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.Dimensions.Lookups.Center",href:"#DimensionalData.Dimensions.Lookups.Center"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Center")],-1)),s[148]||(s[148]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=t(`
julia
Center <: Position
 
-Center()
Used to specify lookup values correspond to the center locus in an interval.
source
`,3))]),i("details",ps,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.Dimensions.Lookups.Start",href:"#DimensionalData.Dimensions.Lookups.Start"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Start")],-1)),s[151]||(s[151]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=t(`
julia
Start <: Position
+Center()
Used to specify lookup values correspond to the center locus in an interval.
source
`,3))]),i("details",ps,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.Dimensions.Lookups.Start",href:"#DimensionalData.Dimensions.Lookups.Start"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Start")],-1)),s[151]||(s[151]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=t(`
julia
Start <: Position
 
-Start()
Used to specify lookup values correspond to the start locus of an interval.
source
`,3))]),i("details",os,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.Dimensions.Lookups.Begin",href:"#DimensionalData.Dimensions.Lookups.Begin"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Begin")],-1)),s[154]||(s[154]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[155]||(s[155]=t(`
julia
Begin <: Position
+Start()
Used to specify lookup values correspond to the start locus of an interval.
source
`,3))]),i("details",os,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.Dimensions.Lookups.Begin",href:"#DimensionalData.Dimensions.Lookups.Begin"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Begin")],-1)),s[154]||(s[154]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[155]||(s[155]=t(`
julia
Begin <: Position
 
-Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
`,4))]),i("details",hs,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.Dimensions.Lookups.End",href:"#DimensionalData.Dimensions.Lookups.End"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.End")],-1)),s[157]||(s[157]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[158]||(s[158]=t(`
julia
End <: Position
+Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
`,4))]),i("details",hs,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.Dimensions.Lookups.End",href:"#DimensionalData.Dimensions.Lookups.End"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.End")],-1)),s[157]||(s[157]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[158]||(s[158]=t(`
julia
End <: Position
 
-End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
`,4))]),i("details",ks,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoPosition",href:"#DimensionalData.Dimensions.Lookups.AutoPosition"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoPosition")],-1)),s[160]||(s[160]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=t(`
julia
AutoPosition <: Position
+End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
`,4))]),i("details",ks,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoPosition",href:"#DimensionalData.Dimensions.Lookups.AutoPosition"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoPosition")],-1)),s[160]||(s[160]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=t(`
julia
AutoPosition <: Position
 
-AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
`,3))]),s[183]||(s[183]=i("h2",{id:"metadata",tabindex:"-1"},[a("Metadata "),i("a",{class:"header-anchor",href:"#metadata","aria-label":'Permalink to "Metadata"'},"​")],-1)),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractMetadata",href:"#DimensionalData.Dimensions.Lookups.AbstractMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractMetadata")],-1)),s[163]||(s[163]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[164]||(s[164]=t('
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
',5))]),i("details",ds,[i("summary",null,[s[165]||(s[165]=i("a",{id:"DimensionalData.Dimensions.Lookups.Metadata",href:"#DimensionalData.Dimensions.Lookups.Metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Metadata")],-1)),s[166]||(s[166]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[167]||(s[167]=t(`
julia
Metadata <: AbstractMetadata
+AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
`,3))]),s[183]||(s[183]=i("h2",{id:"metadata",tabindex:"-1"},[a("Metadata "),i("a",{class:"header-anchor",href:"#metadata","aria-label":'Permalink to "Metadata"'},"​")],-1)),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractMetadata",href:"#DimensionalData.Dimensions.Lookups.AbstractMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractMetadata")],-1)),s[163]||(s[163]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[164]||(s[164]=t('
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
',5))]),i("details",ds,[i("summary",null,[s[165]||(s[165]=i("a",{id:"DimensionalData.Dimensions.Lookups.Metadata",href:"#DimensionalData.Dimensions.Lookups.Metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Metadata")],-1)),s[166]||(s[166]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[167]||(s[167]=t(`
julia
Metadata <: AbstractMetadata
 
 Metadata{X}(val::Union{Dict,NamedTuple})
 Metadata{X}(pairs::Pair...) => Metadata{Dict}
-Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
`,3))]),i("details",gs,[i("summary",null,[s[168]||(s[168]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoMetadata",href:"#DimensionalData.Dimensions.Lookups.NoMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoMetadata")],-1)),s[169]||(s[169]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[170]||(s[170]=t(`
julia
NoMetadata <: AbstractMetadata
+Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
`,3))]),i("details",gs,[i("summary",null,[s[168]||(s[168]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoMetadata",href:"#DimensionalData.Dimensions.Lookups.NoMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoMetadata")],-1)),s[169]||(s[169]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[170]||(s[170]=t(`
julia
NoMetadata <: AbstractMetadata
 
-NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
`,3))]),i("details",cs,[i("summary",null,[s[171]||(s[171]=i("a",{id:"DimensionalData.Dimensions.Lookups.units",href:"#DimensionalData.Dimensions.Lookups.units"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.units")],-1)),s[172]||(s[172]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[173]||(s[173]=t(`
julia
units(x) => Union{Nothing,Any}
+NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
`,3))]),i("details",cs,[i("summary",null,[s[171]||(s[171]=i("a",{id:"DimensionalData.Dimensions.Lookups.units",href:"#DimensionalData.Dimensions.Lookups.units"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.units")],-1)),s[172]||(s[172]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[173]||(s[173]=t(`
julia
units(x) => Union{Nothing,Any}
 units(xs:Tuple) => Tuple
 unit(A::AbstractDimArray, dims::Tuple) => Tuple
-unit(A::AbstractDimArray, dim) => Union{Nothing,Any}
Get the units of an array or Dimension, or a tuple of of either.
Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,5))])])}const fs=l(k,[["render",us]]);export{bs as __pageData,fs as default};
+unit(A::AbstractDimArray, dim) => Union{Nothing,Any}

Get the units of an array or Dimension, or a tuple of of either.

Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.

Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.

source

`,5))])])}const fs=l(k,[["render",us]]);export{bs as __pageData,fs as default}; diff --git a/dev/assets/api_lookuparrays.md.4gWyIReH.lean.js b/dev/assets/api_lookuparrays.md.QQHTyv_S.lean.js similarity index 96% rename from dev/assets/api_lookuparrays.md.4gWyIReH.lean.js rename to dev/assets/api_lookuparrays.md.QQHTyv_S.lean.js index 3453e5208..7e142badc 100644 --- a/dev/assets/api_lookuparrays.md.4gWyIReH.lean.js +++ b/dev/assets/api_lookuparrays.md.QQHTyv_S.lean.js @@ -1,5 +1,5 @@ -import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framework.B8l2DGq3.js";const bs=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),k={name:"api/lookuparrays.md"},r={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},os={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},gs={class:"jldocstring custom-block",open:""},cs={class:"jldocstring custom-block",open:""};function us(ys,s,Es,ms,Ds,Fs){const n=o("Badge");return h(),p("div",null,[s[174]||(s[174]=i("h1",{id:"lookups",tabindex:"-1"},[a("Lookups "),i("a",{class:"header-anchor",href:"#lookups","aria-label":'Permalink to "Lookups"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions.Lookups",href:"#DimensionalData.Dimensions.Lookups"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Lookups

Module for Lookups and Selectors used in DimensionalData.jl

Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.

For example, these allow tracking over array order so fast indexing works even when the array is reversed.

To load Lookup types and methods into scope:

julia
using DimensionalData
-using DimensionalData.Lookups

source

`,7))]),i("details",d,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Lookups.Lookup",href:"#DimensionalData.Dimensions.Lookups.Lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Lookup")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t('
julia
Lookup

Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.

A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.

source

',4))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.Lookups.Aligned",href:"#DimensionalData.Dimensions.Lookups.Aligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Aligned")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
Aligned <: Lookup

Abstract supertype for Lookups where the lookup is aligned with the array axes.

This is by far the most common supertype for Lookup.

source

',4))]),i("details",c,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractSampled",href:"#DimensionalData.Dimensions.Lookups.AbstractSampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractSampled")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
AbstractSampled <: Aligned

Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.

AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.

source

',4))]),i("details",u,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampled",href:"#DimensionalData.Dimensions.Lookups.Sampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampled")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t(`
julia
Sampled <: AbstractSampled
+import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framework.BAAK0EyE.js";const bs=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),k={name:"api/lookuparrays.md"},r={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},os={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},gs={class:"jldocstring custom-block",open:""},cs={class:"jldocstring custom-block",open:""};function us(ys,s,Es,ms,Ds,Fs){const n=o("Badge");return h(),p("div",null,[s[174]||(s[174]=i("h1",{id:"lookups",tabindex:"-1"},[a("Lookups "),i("a",{class:"header-anchor",href:"#lookups","aria-label":'Permalink to "Lookups"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.Dimensions.Lookups",href:"#DimensionalData.Dimensions.Lookups"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups")],-1)),s[1]||(s[1]=a()),e(n,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[2]||(s[2]=t(`
julia
Lookups
Module for Lookups and Selectors used in DimensionalData.jl
Lookup defines traits and AbstractArray wrappers that give specific behaviours for a lookup index when indexed with Selector.
For example, these allow tracking over array order so fast indexing works even when the array is reversed.
To load Lookup types and methods into scope:
julia
using DimensionalData
+using DimensionalData.Lookups
source
`,7))]),i("details",d,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.Dimensions.Lookups.Lookup",href:"#DimensionalData.Dimensions.Lookups.Lookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Lookup")],-1)),s[4]||(s[4]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=t('
julia
Lookup
Types defining the behaviour of a lookup index, how it is plotted and how Selectors like Between work.
A Lookup may be NoLookup indicating that there are no lookup values, Categorical for ordered or unordered categories, or a Sampled index for Points or Intervals.
source
',4))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.Dimensions.Lookups.Aligned",href:"#DimensionalData.Dimensions.Lookups.Aligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Aligned")],-1)),s[7]||(s[7]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=t('
julia
Aligned <: Lookup
Abstract supertype for Lookups where the lookup is aligned with the array axes.
This is by far the most common supertype for Lookup.
source
',4))]),i("details",c,[i("summary",null,[s[9]||(s[9]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractSampled",href:"#DimensionalData.Dimensions.Lookups.AbstractSampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractSampled")],-1)),s[10]||(s[10]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[11]||(s[11]=t('
julia
AbstractSampled <: Aligned
Abstract supertype for Lookups where the lookup is aligned with the array, and is independent of other dimensions. Sampled is provided by this package.
AbstractSampled must have order, span and sampling fields, or a rebuild method that accepts them as keyword arguments.
source
',4))]),i("details",u,[i("summary",null,[s[12]||(s[12]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampled",href:"#DimensionalData.Dimensions.Lookups.Sampled"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampled")],-1)),s[13]||(s[13]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[14]||(s[14]=t(`
julia
Sampled <: AbstractSampled
 
 Sampled(data::AbstractVector, order::Order, span::Span, sampling::Sampling, metadata)
 Sampled(data=AutoValues(); order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata())
A concrete implementation of the Lookup AbstractSampled. It can be used to represent Points or Intervals.
Sampled is capable of representing gridded data from a wide range of sources, allowing correct bounds and Selectors for points or intervals of regular, irregular, forward and reverse lookups.
On AbstractDimArray construction, Sampled lookup is assigned for all lookups of AbstractRange not assigned to Categorical.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with Interval sampling, and Regular span for a vector with known spacing.
We set the locus of the Intervals to Start specifying that the lookup values are for the locus at the start of each interval.
julia
using DimensionalData, DimensionalData.Lookups
@@ -20,9 +20,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   80    1.0  1.0  1.0   1.0
   60    1.0  1.0  1.0   1.0
   40    1.0  1.0  1.0   1.0
-  20    1.0  1.0  1.0   1.0
source
`,11))]),i("details",y,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCyclic",href:"#DimensionalData.Dimensions.Lookups.AbstractCyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCyclic")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
',4))]),i("details",E,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.Lookups.Cyclic",href:"#DimensionalData.Dimensions.Lookups.Cyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Cyclic")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t(`
julia
Cyclic <: AbstractCyclic
+  20    1.0  1.0  1.0   1.0
source
`,11))]),i("details",y,[i("summary",null,[s[15]||(s[15]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCyclic",href:"#DimensionalData.Dimensions.Lookups.AbstractCyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCyclic")],-1)),s[16]||(s[16]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[17]||(s[17]=t('
julia
AbstractCyclic <: AbstractSampled
An abstract supertype for cyclic lookups.
These are AbstractSampled lookups that are cyclic for Selectors.
source
',4))]),i("details",E,[i("summary",null,[s[18]||(s[18]=i("a",{id:"DimensionalData.Dimensions.Lookups.Cyclic",href:"#DimensionalData.Dimensions.Lookups.Cyclic"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Cyclic")],-1)),s[19]||(s[19]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[20]||(s[20]=t(`
julia
Cyclic <: AbstractCyclic
 
-Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
`,8))]),i("details",m,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCategorical",href:"#DimensionalData.Dimensions.Lookups.AbstractCategorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCategorical")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
',5))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.Lookups.Categorical",href:"#DimensionalData.Dimensions.Lookups.Categorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Categorical")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
Categorical <: AbstractCategorical
+Cyclic(data; order=AutoOrder(), span=AutoSpan(), sampling=Points(), metadata=NoMetadata(), cycle)
A Cyclic lookup is similar to Sampled but out of range Selectors At, Near, Contains will cycle the values to typemin or typemax over the length of cycle. Where and .. work as for Sampled.
This is useful when we are using mean annual datasets over a real time-span, or for wrapping longitudes so that -360 and 360 are the same.
Arguments
  • data: An AbstractVector of lookup values, matching the length of the curresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. These can be provided explicitly if they are known and performance is important.
  • span: indicates the size of intervals or distance between points, and will be set to Regular for AbstractRange and Irregular for AbstractArray, unless assigned manually.
  • sampling: is assigned to Points, unless set to Intervals manually. Using Intervals will change the behaviour of bounds and Selectorss to take account for the full size of the interval, rather than the point alone.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
  • cycle: the length of the cycle. This does not have to exactly match the data, the step size is Week(1) the cycle can be Years(1).
Notes
  1. If you use dates and e.g. cycle over a Year, every year will have the number and spacing of Weeks and Days as the cycle year. Using At may not be reliable in terms of exact dates, as it will be applied to the specified date plus or minus n years.
  2. Indexing into a Cycled with any AbstractArray or AbstractRange will return a Sampled as the full cycle is likely no longer available.
  3. .. or Between selectors do not work in a cycled way: they work as for Sampled. This may change in future to return cycled values, but there are problems with this, such as leap years breaking correct date cycling of a single year. If you actually need this behaviour, please make a GitHub issue.
source
`,8))]),i("details",m,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractCategorical",href:"#DimensionalData.Dimensions.Lookups.AbstractCategorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractCategorical")],-1)),s[22]||(s[22]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[23]||(s[23]=t('
julia
AbstractCategorical <: Aligned
Lookups where the values are categories.
Categorical is the provided concrete implementation. But this can easily be extended, all methods are defined for AbstractCategorical.
All AbstractCategorical must provide a rebuild method with data, order and metadata keyword arguments.
source
',5))]),i("details",D,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.Lookups.Categorical",href:"#DimensionalData.Dimensions.Lookups.Categorical"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Categorical")],-1)),s[25]||(s[25]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[26]||(s[26]=t(`
julia
Categorical <: AbstractCategorical
 
 Categorical(o::Order)
 Categorical(; order=Unordered())
A Lookup where the values are categories.
This will be automatically assigned if the lookup contains AbstractString, Symbol or Char. Otherwise it can be assigned manually.
Order will be determined automatically where possible.
Arguments
  • data: An AbstractVector matching the length of the corresponding array axis.
  • order: Order) indicating the order of the lookup, AutoOrder by default, detected from the order of data to be ForwardOrdered, ReverseOrdered or Unordered. Can be provided if this is known and performance is important.
  • metadata: a Dict or Metadata wrapper that holds any metadata object adding more information about the array axis - useful for extending DimensionalData for specific contexts, like geospatial data in Rasters.jl. By default it is NoMetadata().
Example
Create an array with [Interval] sampling.
julia
using DimensionalData
@@ -34,7 +34,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 # output
 
 Categorical{String} ["one", "two", "three"] Unordered,
-Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
`,10))]),i("details",F,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unaligned",href:"#DimensionalData.Dimensions.Lookups.Unaligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unaligned")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t('
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
',4))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Lookups.Transformed",href:"#DimensionalData.Dimensions.Lookups.Transformed"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Transformed")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Transformed <: Unaligned
+Categorical{Symbol} [:a, :b, :c, :d] ForwardOrdered
source
`,10))]),i("details",F,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unaligned",href:"#DimensionalData.Dimensions.Lookups.Unaligned"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unaligned")],-1)),s[28]||(s[28]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[29]||(s[29]=t('
julia
Unaligned <: Lookup
Abstract supertype for Lookup where the lookup is not aligned to the grid.
Indexing an Unaligned with Selectors must provide all other Unaligned dimensions.
source
',4))]),i("details",C,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.Lookups.Transformed",href:"#DimensionalData.Dimensions.Lookups.Transformed"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Transformed")],-1)),s[31]||(s[31]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=t(`
julia
Transformed <: Unaligned
 
 Transformed(f, dim::Dimension; metadata=NoMetadata())
Lookup that uses an affine transformation to convert dimensions from dims(lookup) to dims(array). This can be useful when the dimensions are e.g. rotated from a more commonly used axis.
Any function can be used to do the transformation, but transformations from CoordinateTransformations.jl may be useful.
Arguments
  • f: transformation function
  • dim: a dimension to transform to.
Keyword Arguments
  • metadata:
Example
julia
using DimensionalData, DimensionalData.Lookups, CoordinateTransformations
 
@@ -47,9 +47,9 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 da[X(At(6.0)), Y(At(2.0))]
 
 # output
-9
source
`,10))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.MergedLookup",href:"#DimensionalData.Dimensions.MergedLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.MergedLookup")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t(`
julia
MergedLookup <: Lookup
+9
source
`,10))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.MergedLookup",href:"#DimensionalData.Dimensions.MergedLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.MergedLookup")],-1)),s[34]||(s[34]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=t(`
julia
MergedLookup <: Lookup
 
-MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
`,8))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoLookup",href:"#DimensionalData.Dimensions.Lookups.NoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoLookup")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
NoLookup <: Lookup
+MergedLookup(data, dims; [metadata])
A Lookup that holds multiple combined dimensions.
MergedLookup can be indexed with Selectors like At, Between, and Where although Near has undefined meaning.
Arguments
  • data: A Vector of Tuple.
  • dims: A Tuple of Dimension indicating the dimensions in the tuples in data.
Keywords
  • metadata: a Dict or Metadata object to attach dimension metadata.
source
`,8))]),i("details",f,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoLookup",href:"#DimensionalData.Dimensions.Lookups.NoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoLookup")],-1)),s[37]||(s[37]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[38]||(s[38]=t(`
julia
NoLookup <: Lookup
 
 NoLookup()
A Lookup that is identical to the array axis. Selectors can't be used on this lookup.
Example
Defining a DimArray without passing lookup values to the dimensions, it will be assigned NoLookup:
julia
using DimensionalData
 
@@ -64,26 +64,26 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 
 # output
 
-NoLookup, NoLookup
source
`,8))]),i("details",v,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoLookup",href:"#DimensionalData.Dimensions.Lookups.AutoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoLookup")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AutoLookup <: Lookup
+NoLookup, NoLookup
source
`,8))]),i("details",v,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoLookup",href:"#DimensionalData.Dimensions.Lookups.AutoLookup"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoLookup")],-1)),s[40]||(s[40]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[41]||(s[41]=t(`
julia
AutoLookup <: Lookup
 
 AutoLookup()
-AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
`,4))]),i("details",j,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoValues",href:"#DimensionalData.Dimensions.Lookups.AutoValues"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoValues")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=t('
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
',3))]),s[175]||(s[175]=i("p",null,[a("The generic value getter "),i("code",null,"val")],-1)),i("details",B,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.Lookups.val",href:"#DimensionalData.Dimensions.Lookups.val"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.val")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
val(x)
-val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
`,5))]),s[176]||(s[176]=i("p",null,"Lookup methods:",-1)),i("details",L,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.Lookups.bounds",href:"#DimensionalData.Dimensions.Lookups.bounds"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.bounds")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
+AutoLookup(values=AutoValues(); kw...)
Automatic Lookup, the default lookup. It will be converted automatically to another Lookup when it is possible to detect it from the lookup values.
Keywords will be used in the detected Lookup constructor.
source
`,4))]),i("details",j,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoValues",href:"#DimensionalData.Dimensions.Lookups.AutoValues"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoValues")],-1)),s[43]||(s[43]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=t('
julia
AutoValues
Detect Lookup values from the context. This is used in NoLookup to simply use the array axis as the index when the array is constructed, and in set to change the Lookup type without changing the index values.
source
',3))]),s[175]||(s[175]=i("p",null,[a("The generic value getter "),i("code",null,"val")],-1)),i("details",B,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.Dimensions.Lookups.val",href:"#DimensionalData.Dimensions.Lookups.val"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.val")],-1)),s[46]||(s[46]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[47]||(s[47]=t(`
julia
val(x)
+val(dims::Tuple) => Tuple
Return the contained value of a wrapper object.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
Objects that don't define a val method are returned unaltered.
source
`,5))]),s[176]||(s[176]=i("p",null,"Lookup methods:",-1)),i("details",L,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.Dimensions.Lookups.bounds",href:"#DimensionalData.Dimensions.Lookups.bounds"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.bounds")],-1)),s[49]||(s[49]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[50]||(s[50]=t(`
julia
bounds(xs, [dims::Tuple]) => Tuple{Vararg{Tuple{T,T}}}
 bounds(xs::Tuple) => Tuple{Vararg{Tuple{T,T}}}
 bounds(x, dim) => Tuple{T,T}
-bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
`,5))]),i("details",A,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.Lookups.hasselection",href:"#DimensionalData.Dimensions.Lookups.hasselection"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.hasselection")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
hasselection(x, selector) => Bool
-hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
`,3))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.Lookups.sampling",href:"#DimensionalData.Dimensions.Lookups.sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.sampling")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
sampling(x, [dims::Tuple]) => Tuple
+bounds(dim::Union{Dimension,Lookup}) => Tuple{T,T}
Return the bounds of all dimensions of an object, of a specific dimension, or of a tuple of dimensions.
If bounds are not known, one or both values may be nothing.
dims can be a Dimension, a dimension type, or a tuple of either.
source
`,5))]),i("details",A,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.Dimensions.Lookups.hasselection",href:"#DimensionalData.Dimensions.Lookups.hasselection"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.hasselection")],-1)),s[52]||(s[52]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[53]||(s[53]=t(`
julia
hasselection(x, selector) => Bool
+hasselection(x, selectors::Tuple) => Bool
Check if indexing into x with selectors can be performed, where x is some object with a dims method, and selectors is a Selector or Dimension or a tuple of either.
source
`,3))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.Dimensions.Lookups.sampling",href:"#DimensionalData.Dimensions.Lookups.sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.sampling")],-1)),s[55]||(s[55]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[56]||(s[56]=t(`
julia
sampling(x, [dims::Tuple]) => Tuple
 sampling(x, dim) => Sampling
 sampling(xs::Tuple) => Tuple{Vararg{Sampling}}
-sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.Lookups.span",href:"#DimensionalData.Dimensions.Lookups.span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.span")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
span(x, [dims::Tuple]) => Tuple
+sampling(x:Union{Dimension,Lookup}) => Sampling
Return the Sampling for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.Dimensions.Lookups.span",href:"#DimensionalData.Dimensions.Lookups.span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.span")],-1)),s[58]||(s[58]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[59]||(s[59]=t(`
julia
span(x, [dims::Tuple]) => Tuple
 span(x, dim) => Span
 span(xs::Tuple) => Tuple{Vararg{Span,N}}
-span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",w,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.Lookups.order",href:"#DimensionalData.Dimensions.Lookups.order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.order")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
order(x, [dims::Tuple]) => Tuple
+span(x::Union{Dimension,Lookup}) => Span
Return the Span for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",w,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.Dimensions.Lookups.order",href:"#DimensionalData.Dimensions.Lookups.order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.order")],-1)),s[61]||(s[61]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[62]||(s[62]=t(`
julia
order(x, [dims::Tuple]) => Tuple
 order(xs::Tuple) => Tuple
-order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.Lookups.locus",href:"#DimensionalData.Dimensions.Lookups.locus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.locus")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t(`
julia
locus(x, [dims::Tuple]) => Tuple
+order(x::Union{Dimension,Lookup}) => Order
Return the Ordering of the dimension lookup for each dimension: ForwardOrdered, ReverseOrdered, or Unordered
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DimensionalData.Dimensions.Lookups.locus",href:"#DimensionalData.Dimensions.Lookups.locus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.locus")],-1)),s[64]||(s[64]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=t(`
julia
locus(x, [dims::Tuple]) => Tuple
 locus(x, dim) => Locus
 locus(xs::Tuple) => Tuple{Vararg{Locus,N}}
-locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",O,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.Lookups.shiftlocus",href:"#DimensionalData.Dimensions.Lookups.shiftlocus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.shiftlocus")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
',4))]),s[177]||(s[177]=i("h2",{id:"selectors",tabindex:"-1"},[a("Selectors "),i("a",{class:"header-anchor",href:"#selectors","aria-label":'Permalink to "Selectors"'},"​")],-1)),i("details",I,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.Selector",href:"#DimensionalData.Dimensions.Lookups.Selector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Selector")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=t('
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
',10))]),i("details",q,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.Lookups.IntSelector",href:"#DimensionalData.Dimensions.Lookups.IntSelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.IntSelector")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[74]||(s[74]=t('
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
',5))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.Lookups.ArraySelector",href:"#DimensionalData.Dimensions.Lookups.ArraySelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ArraySelector")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[77]||(s[77]=t('
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
',5))]),i("details",P,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.Lookups.At",href:"#DimensionalData.Dimensions.Lookups.At"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.At")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=t(`
julia
At <: IntSelector
+locus(x::Union{Dimension,Lookup}) => Locus
Return the Position of lookup values for each dimension.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",O,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.Dimensions.Lookups.shiftlocus",href:"#DimensionalData.Dimensions.Lookups.shiftlocus"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.shiftlocus")],-1)),s[67]||(s[67]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[68]||(s[68]=t('
julia
shiftlocus(locus::Locus, x)
Shift the values of x from the current locus to the new locus.
We only shift Sampled, Regular or Explicit, Intervals.
source
',4))]),s[177]||(s[177]=i("h2",{id:"selectors",tabindex:"-1"},[a("Selectors "),i("a",{class:"header-anchor",href:"#selectors","aria-label":'Permalink to "Selectors"'},"​")],-1)),i("details",I,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Dimensions.Lookups.Selector",href:"#DimensionalData.Dimensions.Lookups.Selector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Selector")],-1)),s[70]||(s[70]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=t('
julia
Selector
Abstract supertype for all selectors.
Selectors are wrappers that indicate that passed values are not the array indices, but values to be selected from the dimension lookup, such as DateTime objects for a Ti dimension.
Selectors provided in DimensionalData are:
Note: Selectors can be modified using:
  • Not: as in Not(At(x))
And IntervalSets.jl Interval can be used instead of Between
  • ..
  • Interval
  • OpenInterval
  • ClosedInterval
source
',10))]),i("details",q,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.Dimensions.Lookups.IntSelector",href:"#DimensionalData.Dimensions.Lookups.IntSelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.IntSelector")],-1)),s[73]||(s[73]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[74]||(s[74]=t('
julia
IntSelector <: Selector
Abstract supertype for Selectors that return a single Int index.
IntSelectors provided by DimensionalData are:
source
',5))]),i("details",z,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.Dimensions.Lookups.ArraySelector",href:"#DimensionalData.Dimensions.Lookups.ArraySelector"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ArraySelector")],-1)),s[76]||(s[76]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[77]||(s[77]=t('
julia
ArraySelector <: Selector
Abstract supertype for Selectors that return an AbstractArray.
ArraySelectors provided by DimensionalData are:
source
',5))]),i("details",P,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.Dimensions.Lookups.At",href:"#DimensionalData.Dimensions.Lookups.At"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.At")],-1)),s[79]||(s[79]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=t(`
julia
At <: IntSelector
 
 At(x; atol=nothing, rtol=nothing)
 At(a, b; kw...)
Selector that exactly matches the value on the passed-in dimensions, or throws an error. For ranges and arrays, every intermediate value must match an existing value - not just the end points.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Keyword atol is passed to isapprox.
Example
julia
using DimensionalData
@@ -93,7 +93,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 
 # output
 
-5
source
`,7))]),i("details",N,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.Lookups.Near",href:"#DimensionalData.Dimensions.Lookups.Near"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Near")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[83]||(s[83]=t(`
julia
Near <: IntSelector
+5
source
`,7))]),i("details",N,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.Dimensions.Lookups.Near",href:"#DimensionalData.Dimensions.Lookups.Near"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Near")],-1)),s[82]||(s[82]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[83]||(s[83]=t(`
julia
Near <: IntSelector
 
 Near(x)
 Near(a, b)
Selector that selects the nearest index to x.
With Points this is simply the lookup values nearest to the x, however with Intervals it is the interval center nearest to x. This will be offset from the index value for Start and End locus.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between the nearsest value to each of them will be selected.
Example
julia
using DimensionalData
@@ -102,7 +102,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 A[X(Near(23)), Y(Near(5.1))]
 
 # output
-4
source
`,7))]),i("details",R,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.Lookups.Between",href:"#DimensionalData.Dimensions.Lookups.Between"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Between")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[86]||(s[86]=t(`
julia
Between <: ArraySelector
+4
source
`,7))]),i("details",R,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.Dimensions.Lookups.Between",href:"#DimensionalData.Dimensions.Lookups.Between"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Between")],-1)),s[85]||(s[85]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[86]||(s[86]=t(`
julia
Between <: ArraySelector
 
 Between(a, b)
Depreciated: use a..b instead of Between(a, b). Other Interval objects from IntervalSets.jl, like \`OpenInterval(a, b) will also work, giving the correct open/closed boundaries.
Between will e removed in future to avoid clashes with DataFrames.Between.
Selector that retrieve all indices located between 2 values, evaluated with >= for the lower value, and < for the upper value. This means the same value will not be counted twice in 2 adjacent Between selections.
For Intervals the whole interval must be lie between the values. For Points the points must fall between the values. Different Sampling types may give different results with the same input - this is the intended behaviour.
Between for Irregular intervals is a little complicated. The interval is the distance between a value and the next (for Start locus) or previous (for End locus) value.
For Center, we take the mid point between two index values as the start and end of each interval. This may or may not make sense for the values in your index, so use Between with Irregular Intervals(Center()) with caution.
Example
julia
using DimensionalData
 
@@ -118,7 +118,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
`,10))]),i("details",M,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.Lookups.Touches",href:"#DimensionalData.Dimensions.Lookups.Touches"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Touches")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[89]||(s[89]=t(`
julia
Touches <: ArraySelector
+ 20    4  5
source
`,10))]),i("details",M,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.Dimensions.Lookups.Touches",href:"#DimensionalData.Dimensions.Lookups.Touches"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Touches")],-1)),s[88]||(s[88]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[89]||(s[89]=t(`
julia
Touches <: ArraySelector
 
 Touches(a, b)
Selector that retrieves all indices touching the closed interval 2 values, for the maximum possible area that could interact with the supplied range.
This can be better than .. when e.g. subsetting an area to rasterize, as you may wish to include pixels that just touch the area, rather than those that fall within it.
Touches is different to using closed intervals when the lookups also contain intervals - if any of the intervals touch, they are included. With .. they are discarded unless the whole cell interval falls inside the selector interval.
Example
julia
using DimensionalData
 
@@ -133,7 +133,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
 └────────────────────────────────────────────────────────────┘
   ↓ →  5  6
- 20    4  5
source
`,7))]),i("details",U,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.Contains",href:"#DimensionalData.Dimensions.Lookups.Contains"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Contains")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=t(`
julia
Contains <: IntSelector
+ 20    4  5
source
`,7))]),i("details",U,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.Contains",href:"#DimensionalData.Dimensions.Lookups.Contains"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Contains")],-1)),s[91]||(s[91]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=t(`
julia
Contains <: IntSelector
 
 Contains(x)
 Contains(a, b)
Selector that selects the interval the value is contained by. If the interval is not present in the lookup, an error will be thrown.
Can only be used for Intervals or Categorical. For Categorical it falls back to using At. Contains should not be confused with Base.contains - use Where(contains(x)) to check for if values are contain in categorical values like strings.
x can be any value to select a single index, or a Vector of values to select vector of indices. If two values a and b are used, the range between them will be selected.
Example
julia
using DimensionalData; const DD = DimensionalData
@@ -143,7 +143,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 A[X(Contains(8)), Y(Contains(6.8))]
 
 # output
-3
source
`,7))]),i("details",X,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.Where",href:"#DimensionalData.Dimensions.Lookups.Where"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Where")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=t(`
julia
Where <: ArraySelector
+3
source
`,7))]),i("details",X,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.Where",href:"#DimensionalData.Dimensions.Lookups.Where"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Where")],-1)),s[94]||(s[94]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=t(`
julia
Where <: ArraySelector
 
 Where(f::Function)
Selector that filters a dimension lookup by any function that accepts a single value and returns a Bool.
Example
julia
using DimensionalData
 
@@ -159,7 +159,7 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
   → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
 └─────────────────────────────────────────────────────────────┘
   ↓ →  19  21
- 20     4   6
source
`,5))]),i("details",Y,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.All",href:"#DimensionalData.Dimensions.Lookups.All"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.All")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[98]||(s[98]=t(`
julia
All <: Selector
+ 20     4   6
source
`,5))]),i("details",Y,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.Dimensions.Lookups.All",href:"#DimensionalData.Dimensions.Lookups.All"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.All")],-1)),s[97]||(s[97]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[98]||(s[98]=t(`
julia
All <: Selector
 
 All(selectors::Selector...)
Selector that combines the results of other selectors. The indices used will be the union of all result sorted in ascending order.
Example
julia
using DimensionalData, Unitful
 
@@ -177,42 +177,42 @@ import{_ as l,c as p,j as i,a,G as e,a4 as t,B as o,o as h}from"./chunks/framewo
 └──────────────────────────────────────────────────────────────────────────────┘
   ↓ →  1 s  6 s  91 s  96 s
  10.0    1    2    19    20
- 50.0    3    6    57    60
source
`,5))]),s[178]||(s[178]=i("h2",{id:"Lookup-traits",tabindex:"-1"},[a("Lookup traits "),i("a",{class:"header-anchor",href:"#Lookup-traits","aria-label":'Permalink to "Lookup traits {#Lookup-traits}"'},"​")],-1)),i("details",V,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.Lookups.LookupTrait",href:"#DimensionalData.Dimensions.Lookups.LookupTrait"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.LookupTrait")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=t('
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
',5))]),s[179]||(s[179]=i("h3",{id:"order",tabindex:"-1"},[a("Order "),i("a",{class:"header-anchor",href:"#order","aria-label":'Permalink to "Order"'},"​")],-1)),i("details",W,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.Lookups.Order",href:"#DimensionalData.Dimensions.Lookups.Order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Order")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=t('
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
',3))]),i("details",G,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.Lookups.Ordered",href:"#DimensionalData.Dimensions.Lookups.Ordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Ordered")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=t('
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
',3))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.Dimensions.Lookups.ForwardOrdered",href:"#DimensionalData.Dimensions.Lookups.ForwardOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ForwardOrdered")],-1)),s[109]||(s[109]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=t(`
julia
ForwardOrdered <: Ordered
+ 50.0    3    6    57    60
source
`,5))]),s[178]||(s[178]=i("h2",{id:"Lookup-traits",tabindex:"-1"},[a("Lookup traits "),i("a",{class:"header-anchor",href:"#Lookup-traits","aria-label":'Permalink to "Lookup traits {#Lookup-traits}"'},"​")],-1)),i("details",V,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.Dimensions.Lookups.LookupTrait",href:"#DimensionalData.Dimensions.Lookups.LookupTrait"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.LookupTrait")],-1)),s[100]||(s[100]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=t('
julia
LookupTrait
Abstract supertype of all traits of a Lookup.
These modify the behaviour of the lookup index.
The term "Trait" is used loosely - these may be fields of an object of traits hard-coded to specific types.
source
',5))]),s[179]||(s[179]=i("h3",{id:"order",tabindex:"-1"},[a("Order "),i("a",{class:"header-anchor",href:"#order","aria-label":'Permalink to "Order"'},"​")],-1)),i("details",W,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.Dimensions.Lookups.Order",href:"#DimensionalData.Dimensions.Lookups.Order"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Order")],-1)),s[103]||(s[103]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=t('
julia
Order <: LookupTrait
Traits for the order of a Lookup. These determine how searchsorted finds values in the index, and how objects are plotted.
source
',3))]),i("details",G,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.Dimensions.Lookups.Ordered",href:"#DimensionalData.Dimensions.Lookups.Ordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Ordered")],-1)),s[106]||(s[106]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=t('
julia
Ordered <: Order
Supertype for the order of an ordered Lookup, including ForwardOrdered and ReverseOrdered.
source
',3))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.Dimensions.Lookups.ForwardOrdered",href:"#DimensionalData.Dimensions.Lookups.ForwardOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ForwardOrdered")],-1)),s[109]||(s[109]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=t(`
julia
ForwardOrdered <: Ordered
 
-ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
`,3))]),i("details",$,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.Dimensions.Lookups.ReverseOrdered",href:"#DimensionalData.Dimensions.Lookups.ReverseOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ReverseOrdered")],-1)),s[112]||(s[112]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=t(`
julia
ReverseOrdered <: Ordered
+ForwardOrdered()
Indicates that the Lookup index is in the normal forward order.
source
`,3))]),i("details",$,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.Dimensions.Lookups.ReverseOrdered",href:"#DimensionalData.Dimensions.Lookups.ReverseOrdered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.ReverseOrdered")],-1)),s[112]||(s[112]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=t(`
julia
ReverseOrdered <: Ordered
 
-ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
`,3))]),i("details",H,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unordered",href:"#DimensionalData.Dimensions.Lookups.Unordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unordered")],-1)),s[115]||(s[115]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=t(`
julia
Unordered <: Order
+ReverseOrdered()
Indicates that the Lookup index is in the reverse order.
source
`,3))]),i("details",H,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.Dimensions.Lookups.Unordered",href:"#DimensionalData.Dimensions.Lookups.Unordered"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Unordered")],-1)),s[115]||(s[115]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=t(`
julia
Unordered <: Order
 
-Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
`,4))]),i("details",J,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoOrder",href:"#DimensionalData.Dimensions.Lookups.AutoOrder"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoOrder")],-1)),s[118]||(s[118]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=t(`
julia
AutoOrder <: Order
+Unordered()
Indicates that Lookup is unordered.
This means the index cannot be searched with searchsortedfirst or similar optimised methods - instead it will use findfirst.
source
`,4))]),i("details",J,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoOrder",href:"#DimensionalData.Dimensions.Lookups.AutoOrder"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoOrder")],-1)),s[118]||(s[118]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=t(`
julia
AutoOrder <: Order
 
-AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
`,3))]),s[180]||(s[180]=i("h3",{id:"span",tabindex:"-1"},[a("Span "),i("a",{class:"header-anchor",href:"#span","aria-label":'Permalink to "Span"'},"​")],-1)),i("details",Q,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.Dimensions.Lookups.Span",href:"#DimensionalData.Dimensions.Lookups.Span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Span")],-1)),s[121]||(s[121]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=t('
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
',3))]),i("details",Z,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.Dimensions.Lookups.Regular",href:"#DimensionalData.Dimensions.Lookups.Regular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Regular")],-1)),s[124]||(s[124]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=t(`
julia
Regular <: Span
+AutoOrder()
Specifies that the Order of a Lookup will be found automatically where possible.
source
`,3))]),s[180]||(s[180]=i("h3",{id:"span",tabindex:"-1"},[a("Span "),i("a",{class:"header-anchor",href:"#span","aria-label":'Permalink to "Span"'},"​")],-1)),i("details",Q,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.Dimensions.Lookups.Span",href:"#DimensionalData.Dimensions.Lookups.Span"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Span")],-1)),s[121]||(s[121]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=t('
julia
Span <: LookupTrait
Defines the type of span used in a Sampling index. These are Regular or Irregular.
source
',3))]),i("details",Z,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.Dimensions.Lookups.Regular",href:"#DimensionalData.Dimensions.Lookups.Regular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Regular")],-1)),s[124]||(s[124]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=t(`
julia
Regular <: Span
 
-Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
`,3))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.Dimensions.Lookups.Irregular",href:"#DimensionalData.Dimensions.Lookups.Irregular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Irregular")],-1)),s[127]||(s[127]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[128]||(s[128]=t(`
julia
Irregular <: Span
+Regular(step=AutoStep())
Points or Intervals that have a fixed, regular step.
source
`,3))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.Dimensions.Lookups.Irregular",href:"#DimensionalData.Dimensions.Lookups.Irregular"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Irregular")],-1)),s[127]||(s[127]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[128]||(s[128]=t(`
julia
Irregular <: Span
 
 Irregular(bounds::Tuple)
-Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
`,3))]),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"DimensionalData.Dimensions.Lookups.Explicit",href:"#DimensionalData.Dimensions.Lookups.Explicit"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Explicit")],-1)),s[130]||(s[130]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[131]||(s[131]=t('
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
',4))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoSpan",href:"#DimensionalData.Dimensions.Lookups.AutoSpan"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoSpan")],-1)),s[133]||(s[133]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[134]||(s[134]=t(`
julia
AutoSpan <: Span
+Irregular(lowerbound, upperbound)
Points or Intervals that have an Irregular step size. To enable bounds tracking and accurate selectors, the starting bounds are provided as a 2 tuple, or 2 arguments. (nothing, nothing) is acceptable input, the bounds will be guessed from the index, but may be inaccurate.
source
`,3))]),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"DimensionalData.Dimensions.Lookups.Explicit",href:"#DimensionalData.Dimensions.Lookups.Explicit"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Explicit")],-1)),s[130]||(s[130]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[131]||(s[131]=t('
julia
Explicit(bounds::AbstractMatrix)
Intervals where the span is explicitly listed for every interval.
This uses a matrix where with length 2 columns for each index value, holding the lower and upper bounds for that specific index.
source
',4))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoSpan",href:"#DimensionalData.Dimensions.Lookups.AutoSpan"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoSpan")],-1)),s[133]||(s[133]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[134]||(s[134]=t(`
julia
AutoSpan <: Span
 
-AutoSpan()
The span will be guessed and replaced in format or set.
source
`,3))]),s[181]||(s[181]=i("h3",{id:"sampling",tabindex:"-1"},[a("Sampling "),i("a",{class:"header-anchor",href:"#sampling","aria-label":'Permalink to "Sampling"'},"​")],-1)),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampling",href:"#DimensionalData.Dimensions.Lookups.Sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampling")],-1)),s[136]||(s[136]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[137]||(s[137]=t('
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
',3))]),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.Dimensions.Lookups.Points",href:"#DimensionalData.Dimensions.Lookups.Points"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Points")],-1)),s[139]||(s[139]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=t(`
julia
Points <: Sampling
+AutoSpan()
The span will be guessed and replaced in format or set.
source
`,3))]),s[181]||(s[181]=i("h3",{id:"sampling",tabindex:"-1"},[a("Sampling "),i("a",{class:"header-anchor",href:"#sampling","aria-label":'Permalink to "Sampling"'},"​")],-1)),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"DimensionalData.Dimensions.Lookups.Sampling",href:"#DimensionalData.Dimensions.Lookups.Sampling"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Sampling")],-1)),s[136]||(s[136]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[137]||(s[137]=t('
julia
Sampling <: LookupTrait
Indicates the sampling method used by the index: Points or Intervals.
source
',3))]),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.Dimensions.Lookups.Points",href:"#DimensionalData.Dimensions.Lookups.Points"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Points")],-1)),s[139]||(s[139]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=t(`
julia
Points <: Sampling
 
-Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
`,4))]),i("details",es,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Dimensions.Lookups.Intervals",href:"#DimensionalData.Dimensions.Lookups.Intervals"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Intervals")],-1)),s[142]||(s[142]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=t(`
julia
Intervals <: Sampling
+Points()
Sampling lookup where single samples at exact points.
These are always plotted at the center of array cells.
source
`,4))]),i("details",es,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Dimensions.Lookups.Intervals",href:"#DimensionalData.Dimensions.Lookups.Intervals"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Intervals")],-1)),s[142]||(s[142]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=t(`
julia
Intervals <: Sampling
 
-Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
`,4))]),s[182]||(s[182]=i("h3",{id:"positions",tabindex:"-1"},[a("Positions "),i("a",{class:"header-anchor",href:"#positions","aria-label":'Permalink to "Positions"'},"​")],-1)),i("details",ts,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.Dimensions.Lookups.Position",href:"#DimensionalData.Dimensions.Lookups.Position"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Position")],-1)),s[145]||(s[145]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=t('
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
',5))]),i("details",ls,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.Dimensions.Lookups.Center",href:"#DimensionalData.Dimensions.Lookups.Center"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Center")],-1)),s[148]||(s[148]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=t(`
julia
Center <: Position
+Intervals(locus::Position)
Sampling specifying that sampled values are the mean (or similar) value over an interval, rather than at one specific point.
Intervals require a locus of Start, Center or End to define the location in the interval that the index values refer to.
source
`,4))]),s[182]||(s[182]=i("h3",{id:"positions",tabindex:"-1"},[a("Positions "),i("a",{class:"header-anchor",href:"#positions","aria-label":'Permalink to "Positions"'},"​")],-1)),i("details",ts,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.Dimensions.Lookups.Position",href:"#DimensionalData.Dimensions.Lookups.Position"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Position")],-1)),s[145]||(s[145]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=t('
julia
Position <: LookupTrait
Abstract supertype of types that indicate the locus of index values where they represent Intervals.
These allow for values array cells to align with the Start, Center, or End of values in the lookup index.
This means they can be plotted with correct axis markers, and allows automatic conversions to between formats with different standards (such as NetCDF and GeoTiff).
source
',5))]),i("details",ls,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.Dimensions.Lookups.Center",href:"#DimensionalData.Dimensions.Lookups.Center"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Center")],-1)),s[148]||(s[148]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=t(`
julia
Center <: Position
 
-Center()
Used to specify lookup values correspond to the center locus in an interval.
source
`,3))]),i("details",ps,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.Dimensions.Lookups.Start",href:"#DimensionalData.Dimensions.Lookups.Start"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Start")],-1)),s[151]||(s[151]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=t(`
julia
Start <: Position
+Center()
Used to specify lookup values correspond to the center locus in an interval.
source
`,3))]),i("details",ps,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.Dimensions.Lookups.Start",href:"#DimensionalData.Dimensions.Lookups.Start"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Start")],-1)),s[151]||(s[151]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=t(`
julia
Start <: Position
 
-Start()
Used to specify lookup values correspond to the start locus of an interval.
source
`,3))]),i("details",os,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.Dimensions.Lookups.Begin",href:"#DimensionalData.Dimensions.Lookups.Begin"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Begin")],-1)),s[154]||(s[154]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[155]||(s[155]=t(`
julia
Begin <: Position
+Start()
Used to specify lookup values correspond to the start locus of an interval.
source
`,3))]),i("details",os,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.Dimensions.Lookups.Begin",href:"#DimensionalData.Dimensions.Lookups.Begin"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Begin")],-1)),s[154]||(s[154]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[155]||(s[155]=t(`
julia
Begin <: Position
 
-Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
`,4))]),i("details",hs,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.Dimensions.Lookups.End",href:"#DimensionalData.Dimensions.Lookups.End"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.End")],-1)),s[157]||(s[157]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[158]||(s[158]=t(`
julia
End <: Position
+Begin()
Used to specify the begin index of a Dimension axis, as regular begin will not work with named dimensions.
Can be used with : to create a BeginEndRange or BeginEndStepRange.
source
`,4))]),i("details",hs,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.Dimensions.Lookups.End",href:"#DimensionalData.Dimensions.Lookups.End"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.End")],-1)),s[157]||(s[157]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[158]||(s[158]=t(`
julia
End <: Position
 
-End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
`,4))]),i("details",ks,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoPosition",href:"#DimensionalData.Dimensions.Lookups.AutoPosition"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoPosition")],-1)),s[160]||(s[160]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=t(`
julia
AutoPosition <: Position
+End()
Used to specify the end index of a Dimension axis, as regular end will not work with named dimensions. Can be used with : to create a BeginEndRange or BeginEndStepRange.
Also used to specify lookup values correspond to the end locus of an interval.
source
`,4))]),i("details",ks,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.Dimensions.Lookups.AutoPosition",href:"#DimensionalData.Dimensions.Lookups.AutoPosition"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AutoPosition")],-1)),s[160]||(s[160]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=t(`
julia
AutoPosition <: Position
 
-AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
`,3))]),s[183]||(s[183]=i("h2",{id:"metadata",tabindex:"-1"},[a("Metadata "),i("a",{class:"header-anchor",href:"#metadata","aria-label":'Permalink to "Metadata"'},"​")],-1)),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractMetadata",href:"#DimensionalData.Dimensions.Lookups.AbstractMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractMetadata")],-1)),s[163]||(s[163]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[164]||(s[164]=t('
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
',5))]),i("details",ds,[i("summary",null,[s[165]||(s[165]=i("a",{id:"DimensionalData.Dimensions.Lookups.Metadata",href:"#DimensionalData.Dimensions.Lookups.Metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Metadata")],-1)),s[166]||(s[166]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[167]||(s[167]=t(`
julia
Metadata <: AbstractMetadata
+AutoPosition()
Indicates a interval where the index locus is not yet known. This will be filled with a default value on object construction.
source
`,3))]),s[183]||(s[183]=i("h2",{id:"metadata",tabindex:"-1"},[a("Metadata "),i("a",{class:"header-anchor",href:"#metadata","aria-label":'Permalink to "Metadata"'},"​")],-1)),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.Dimensions.Lookups.AbstractMetadata",href:"#DimensionalData.Dimensions.Lookups.AbstractMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.AbstractMetadata")],-1)),s[163]||(s[163]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[164]||(s[164]=t('
julia
AbstractMetadata{X,T}
Abstract supertype for all metadata wrappers.
Metadata wrappers allow tracking the contents and origin of metadata. This can facilitate conversion between metadata types (for saving a file to a different format) or simply saving data back to the same file type with identical metadata.
Using a wrapper instead of Dict or NamedTuple also lets us pass metadata objects to set without ambiguity about where to put them.
source
',5))]),i("details",ds,[i("summary",null,[s[165]||(s[165]=i("a",{id:"DimensionalData.Dimensions.Lookups.Metadata",href:"#DimensionalData.Dimensions.Lookups.Metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.Metadata")],-1)),s[166]||(s[166]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[167]||(s[167]=t(`
julia
Metadata <: AbstractMetadata
 
 Metadata{X}(val::Union{Dict,NamedTuple})
 Metadata{X}(pairs::Pair...) => Metadata{Dict}
-Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
`,3))]),i("details",gs,[i("summary",null,[s[168]||(s[168]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoMetadata",href:"#DimensionalData.Dimensions.Lookups.NoMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoMetadata")],-1)),s[169]||(s[169]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[170]||(s[170]=t(`
julia
NoMetadata <: AbstractMetadata
+Metadata{X}(; kw...) => Metadata{NamedTuple}
General Metadata object. The X type parameter categorises the metadata for method dispatch, if required.
source
`,3))]),i("details",gs,[i("summary",null,[s[168]||(s[168]=i("a",{id:"DimensionalData.Dimensions.Lookups.NoMetadata",href:"#DimensionalData.Dimensions.Lookups.NoMetadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.NoMetadata")],-1)),s[169]||(s[169]=a()),e(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[170]||(s[170]=t(`
julia
NoMetadata <: AbstractMetadata
 
-NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
`,3))]),i("details",cs,[i("summary",null,[s[171]||(s[171]=i("a",{id:"DimensionalData.Dimensions.Lookups.units",href:"#DimensionalData.Dimensions.Lookups.units"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.units")],-1)),s[172]||(s[172]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[173]||(s[173]=t(`
julia
units(x) => Union{Nothing,Any}
+NoMetadata()
Indicates an object has no metadata. But unlike using nothing, get, keys and haskey will still work on it, get always returning the fallback argument. keys returns () while haskey always returns false.
source
`,3))]),i("details",cs,[i("summary",null,[s[171]||(s[171]=i("a",{id:"DimensionalData.Dimensions.Lookups.units",href:"#DimensionalData.Dimensions.Lookups.units"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.units")],-1)),s[172]||(s[172]=a()),e(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[173]||(s[173]=t(`
julia
units(x) => Union{Nothing,Any}
 units(xs:Tuple) => Tuple
 unit(A::AbstractDimArray, dims::Tuple) => Tuple
-unit(A::AbstractDimArray, dim) => Union{Nothing,Any}
Get the units of an array or Dimension, or a tuple of of either.
Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,5))])])}const fs=l(k,[["render",us]]);export{bs as __pageData,fs as default};
+unit(A::AbstractDimArray, dim) => Union{Nothing,Any}

Get the units of an array or Dimension, or a tuple of of either.

Units do not have a set field, and may or may not be included in metadata. This method is to facilitate use in labels and plots when units are available, not a guarantee that they will be. If not available, nothing is returned.

Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.

source

`,5))])])}const fs=l(k,[["render",us]]);export{bs as __pageData,fs as default}; diff --git a/dev/assets/api_reference.md.Cbzd03zL.js b/dev/assets/api_reference.md.CbFETIiO.js similarity index 98% rename from dev/assets/api_reference.md.Cbzd03zL.js rename to dev/assets/api_reference.md.CbFETIiO.js index ce337a054..b7f4d5607 100644 --- a/dev/assets/api_reference.md.Cbzd03zL.js +++ b/dev/assets/api_reference.md.CbFETIiO.js @@ -1,4 +1,4 @@ -import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framework.B8l2DGq3.js";const ms=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),d={name:"api/reference.md"},r={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""};function os(gs,s,Es,ys,cs,Fs){const n=k("Badge");return p(),h("div",null,[s[165]||(s[165]=i("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),i("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=i("h2",{id:"arrays",tabindex:"-1"},[a("Arrays "),i("a",{class:"header-anchor",href:"#arrays","aria-label":'Permalink to "Arrays"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.AbstractBasicDimArray",href:"#DimensionalData.AbstractBasicDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractBasicDimArray")],-1)),s[1]||(s[1]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=l('
julia
AbstractBasicDimArray <: AbstractArray

The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension

Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.

source

',4))]),i("details",o,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.AbstractDimArray",href:"#DimensionalData.AbstractDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimArray")],-1)),s[4]||(s[4]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=l('
julia
AbstractDimArray <: AbstractBasicArray

Abstract supertype for all "dim" arrays.

These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.

parent must return the source array.

They should have metadata, name and refdims methods, although these are optional.

A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.

Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"

source

',8))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.DimArray",href:"#DimensionalData.DimArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimArray")],-1)),s[7]||(s[7]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=l(`
julia
DimArray <: AbstractDimArray
+import{_ as h,c as e,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framework.BAAK0EyE.js";const ms=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),r={name:"api/reference.md"},d={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""};function os(gs,s,Es,ys,cs,Fs){const n=k("Badge");return p(),e("div",null,[s[165]||(s[165]=i("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),i("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=i("h2",{id:"arrays",tabindex:"-1"},[a("Arrays "),i("a",{class:"header-anchor",href:"#arrays","aria-label":'Permalink to "Arrays"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.AbstractBasicDimArray",href:"#DimensionalData.AbstractBasicDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractBasicDimArray")],-1)),s[1]||(s[1]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=l('
julia
AbstractBasicDimArray <: AbstractArray
The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension
Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.
source
',4))]),i("details",o,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.AbstractDimArray",href:"#DimensionalData.AbstractDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimArray")],-1)),s[4]||(s[4]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=l('
julia
AbstractDimArray <: AbstractBasicArray
Abstract supertype for all "dim" arrays.
These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.
parent must return the source array.
They should have metadata, name and refdims methods, although these are optional.
A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
source
',8))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.DimArray",href:"#DimensionalData.DimArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimArray")],-1)),s[7]||(s[7]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=l(`
julia
DimArray <: AbstractDimArray
 
 DimArray(data, dims, refdims, name, metadata)
 DimArray(data, dims::Tuple; refdims=(), name=NoName(), metadata=NoMetadata())
The main concrete subtype of AbstractDimArray.
DimArray maintains and updates its Dimensions through transformations and moves dimensions to reference dimension refdims after reducing operations (like e.g. mean).
Arguments
  • data: An AbstractArray.
  • dims: A Tuple of Dimension
  • name: A string name for the array. Shows in plots and tables.
  • refdims: refence dimensions. Usually set programmatically to track past slices and reductions of dimension for labelling and reconstruction.
  • metadata: Dict or Metadata object, or NoMetadata()
Indexing can be done with all regular indices, or with Dimensions and/or Selectors.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
Example:
julia
julia> using Dates, DimensionalData
@@ -27,7 +27,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  20  0.774092
  30  0.823656
  40  0.637077
- 50  0.692235
source
`,10))]),s[167]||(s[167]=i("p",null,[a("Shorthand "),i("code",null,"AbstractDimArray"),a(" constructors:")],-1)),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Base.fill",href:"#Base.fill"},[i("span",{class:"jlbinding"},"Base.fill")],-1)),s[10]||(s[10]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=l(`
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
+ 50  0.692235
source
`,10))]),s[167]||(s[167]=i("p",null,[a("Shorthand "),i("code",null,"AbstractDimArray"),a(" constructors:")],-1)),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Base.fill",href:"#Base.fill"},[i("span",{class:"jlbinding"},"Base.fill")],-1)),s[10]||(s[10]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=l(`
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
 Base.fill(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray with a fill value of x.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData, Random; Random.seed!(123);
 
 julia> rand(Bool, X(2), Y(4))
@@ -37,7 +37,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ X, → Y
 └──────────────────────┘
  0  0  0  0
- 1  0  0  1
source
`,8))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Base.rand",href:"#Base.rand"},[i("span",{class:"jlbinding"},"Base.rand")],-1)),s[13]||(s[13]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=l(`
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
+ 1  0  0  1
source
`,8))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Base.rand",href:"#Base.rand"},[i("span",{class:"jlbinding"},"Base.rand")],-1)),s[13]||(s[13]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=l(`
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
 Base.rand(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Dimension...; kw...) => DimArray
Create a DimArray of random values.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
@@ -61,7 +61,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0       150.0       200.0
   :a    0.443494    0.253849    0.867547
   :b    0.745673    0.334152    0.0802658
-  :c    0.512083    0.427328    0.311448
source
`,8))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Base.zeros",href:"#Base.zeros"},[i("span",{class:"jlbinding"},"Base.zeros")],-1)),s[16]||(s[16]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=l(`
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
+  :c    0.512083    0.427328    0.311448
source
`,8))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Base.zeros",href:"#Base.zeros"},[i("span",{class:"jlbinding"},"Base.zeros")],-1)),s[16]||(s[16]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=l(`
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
 Base.zeros(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of zeros.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> zeros(Bool, X(2), Y(4))
@@ -83,7 +83,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0  150.0  200.0
   :a    0.0    0.0    0.0
   :b    0.0    0.0    0.0
-  :c    0.0    0.0    0.0
source
`,8))]),i("details",F,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Base.ones",href:"#Base.ones"},[i("span",{class:"jlbinding"},"Base.ones")],-1)),s[19]||(s[19]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=l(`
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
+  :c    0.0    0.0    0.0
source
`,8))]),i("details",F,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Base.ones",href:"#Base.ones"},[i("span",{class:"jlbinding"},"Base.ones")],-1)),s[19]||(s[19]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=l(`
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
 Base.ones(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of ones.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> ones(Bool, X(2), Y(4))
@@ -105,8 +105,8 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0  150.0  200.0
   :a    1.0    1.0    1.0
   :b    1.0    1.0    1.0
-  :c    1.0    1.0    1.0
source
`,8))]),s[168]||(s[168]=i("p",null,"Functions for getting information from objects:",-1)),i("details",C,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.dims",href:"#DimensionalData.Dimensions.dims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[22]||(s[22]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[23]||(s[23]=l(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+  :c    1.0    1.0    1.0
source
`,8))]),s[168]||(s[168]=i("p",null,"Functions for getting information from objects:",-1)),i("details",C,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.dims",href:"#DimensionalData.Dimensions.dims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[22]||(s[22]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[23]||(s[23]=l(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -120,13 +120,13 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
`,13))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.refdims",href:"#DimensionalData.Dimensions.refdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.refdims")],-1)),s[25]||(s[25]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=l(`
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
`,5))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.metadata",href:"#DimensionalData.Dimensions.Lookups.metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.metadata")],-1)),s[28]||(s[28]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[29]||(s[29]=l(`
julia
metadata(x) => (object metadata)
+(↓ X, → Y)
source
`,13))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.refdims",href:"#DimensionalData.Dimensions.refdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.refdims")],-1)),s[25]||(s[25]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=l(`
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
`,5))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.metadata",href:"#DimensionalData.Dimensions.Lookups.metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.metadata")],-1)),s[28]||(s[28]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[29]||(s[29]=l(`
julia
metadata(x) => (object metadata)
 metadata(x, dims::Tuple)  => Tuple (Dimension metadata)
-metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",D,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.name",href:"#DimensionalData.Dimensions.name"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name")],-1)),s[31]||(s[31]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[32]||(s[32]=l(`
julia
name(x) => Symbol
+metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",D,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.name",href:"#DimensionalData.Dimensions.name"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name")],-1)),s[31]||(s[31]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[32]||(s[32]=l(`
julia
name(x) => Symbol
 name(xs:Tuple) => NTuple{N,Symbol}
 name(x, dims::Tuple) => NTuple{N,Symbol}
-name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.otherdims",href:"#DimensionalData.Dimensions.otherdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[34]||(s[34]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[35]||(s[35]=l(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.otherdims",href:"#DimensionalData.Dimensions.otherdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[34]||(s[34]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[35]||(s[35]=l(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -134,7 +134,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
`,8))]),i("details",B,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.dimnum",href:"#DimensionalData.Dimensions.dimnum"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[37]||(s[37]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[38]||(s[38]=l(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
`,8))]),i("details",B,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.dimnum",href:"#DimensionalData.Dimensions.dimnum"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[37]||(s[37]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[38]||(s[38]=l(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -143,7 +143,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
`,8))]),i("details",A,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.hasdim",href:"#DimensionalData.Dimensions.hasdim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[40]||(s[40]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[41]||(s[41]=l(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
`,8))]),i("details",A,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.hasdim",href:"#DimensionalData.Dimensions.hasdim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[40]||(s[40]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[41]||(s[41]=l(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -156,7 +156,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
`,8))]),s[169]||(s[169]=i("h2",{id:"Multi-array-datasets",tabindex:"-1"},[a("Multi-array datasets "),i("a",{class:"header-anchor",href:"#Multi-array-datasets","aria-label":'Permalink to "Multi-array datasets {#Multi-array-datasets}"'},"​")],-1)),i("details",f,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.AbstractDimStack",href:"#DimensionalData.AbstractDimStack"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimStack")],-1)),s[43]||(s[43]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=l('
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
',10))]),i("details",j,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.DimStack",href:"#DimensionalData.DimStack"},[i("span",{class:"jlbinding"},"DimensionalData.DimStack")],-1)),s[46]||(s[46]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[47]||(s[47]=l(`
julia
DimStack <: AbstractDimStack
+false
source
`,8))]),s[169]||(s[169]=i("h2",{id:"Multi-array-datasets",tabindex:"-1"},[a("Multi-array datasets "),i("a",{class:"header-anchor",href:"#Multi-array-datasets","aria-label":'Permalink to "Multi-array datasets {#Multi-array-datasets}"'},"​")],-1)),i("details",f,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.AbstractDimStack",href:"#DimensionalData.AbstractDimStack"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimStack")],-1)),s[43]||(s[43]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=l('
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
',10))]),i("details",j,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.DimStack",href:"#DimensionalData.DimStack"},[i("span",{class:"jlbinding"},"DimensionalData.DimStack")],-1)),s[46]||(s[46]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[47]||(s[47]=l(`
julia
DimStack <: AbstractDimStack
 
 DimStack(data::AbstractDimArray...; kw...)
 DimStack(data::Tuple{Vararg{AbstractDimArray}}; kw...)
@@ -183,7 +183,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (one = 4.0, two = 8.0, three = 12.0)
 
 julia> s[X(At(:a))] isa DimStack
-true
source
`,16))]),s[170]||(s[170]=i("h2",{id:"Dimension-generators",tabindex:"-1"},[a("Dimension generators "),i("a",{class:"header-anchor",href:"#Dimension-generators","aria-label":'Permalink to "Dimension generators {#Dimension-generators}"'},"​")],-1)),i("details",v,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.DimIndices",href:"#DimensionalData.DimIndices"},[i("span",{class:"jlbinding"},"DimensionalData.DimIndices")],-1)),s[49]||(s[49]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[50]||(s[50]=l(`
julia
DimIndices <: AbstractArray
+true
source
`,16))]),s[170]||(s[170]=i("h2",{id:"Dimension-generators",tabindex:"-1"},[a("Dimension generators "),i("a",{class:"header-anchor",href:"#Dimension-generators","aria-label":'Permalink to "Dimension generators {#Dimension-generators}"'},"​")],-1)),i("details",v,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.DimIndices",href:"#DimensionalData.DimIndices"},[i("span",{class:"jlbinding"},"DimensionalData.DimIndices")],-1)),s[49]||(s[49]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[50]||(s[50]=l(`
julia
DimIndices <: AbstractArray
 
 DimIndices(x)
 DimIndices(dims::Tuple)
@@ -220,7 +220,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 └─────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'c'
  0.0  0.9063    0.0991336
- 0.6  0.745673  0.692209
source
`,9))]),i("details",w,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.DimSelectors",href:"#DimensionalData.DimSelectors"},[i("span",{class:"jlbinding"},"DimensionalData.DimSelectors")],-1)),s[52]||(s[52]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[53]||(s[53]=l(`
julia
DimSelectors <: AbstractArray
+ 0.6  0.745673  0.692209
source
`,9))]),i("details",w,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.DimSelectors",href:"#DimensionalData.DimSelectors"},[i("span",{class:"jlbinding"},"DimensionalData.DimSelectors")],-1)),s[52]||(s[52]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[53]||(s[53]=l(`
julia
DimSelectors <: AbstractArray
 
 DimSelectors(x; selectors, atol...)
 DimSelectors(dims::Tuple; selectors, atol...)
@@ -238,11 +238,11 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ →  1.0        11.0       21.0
   1.0  0.691162    0.218579   0.539076
  10.0  0.0303789   0.420756   0.485687
- 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
`,10))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.DimPoints",href:"#DimensionalData.DimPoints"},[i("span",{class:"jlbinding"},"DimensionalData.DimPoints")],-1)),s[55]||(s[55]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l(`
julia
DimPoints <: AbstractArray
+ 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
`,10))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.DimPoints",href:"#DimensionalData.DimPoints"},[i("span",{class:"jlbinding"},"DimensionalData.DimPoints")],-1)),s[55]||(s[55]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l(`
julia
DimPoints <: AbstractArray
 
 DimPoints(x; order)
 DimPoints(dims::Tuple; order)
-DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
`,6))]),s[171]||(s[171]=i("h2",{id:"Tables.jl/TableTraits.jl-interface",tabindex:"-1"},[a("Tables.jl/TableTraits.jl interface "),i("a",{class:"header-anchor",href:"#Tables.jl/TableTraits.jl-interface","aria-label":'Permalink to "Tables.jl/TableTraits.jl interface {#Tables.jl/TableTraits.jl-interface}"'},"​")],-1)),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.AbstractDimTable",href:"#DimensionalData.AbstractDimTable"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimTable")],-1)),s[58]||(s[58]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[59]||(s[59]=l('
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
',3))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.DimTable",href:"#DimensionalData.DimTable"},[i("span",{class:"jlbinding"},"DimensionalData.DimTable")],-1)),s[61]||(s[61]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[62]||(s[62]=l(`
julia
DimTable <: AbstractDimTable
+DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
`,6))]),s[171]||(s[171]=i("h2",{id:"Tables.jl/TableTraits.jl-interface",tabindex:"-1"},[a("Tables.jl/TableTraits.jl interface "),i("a",{class:"header-anchor",href:"#Tables.jl/TableTraits.jl-interface","aria-label":'Permalink to "Tables.jl/TableTraits.jl interface {#Tables.jl/TableTraits.jl-interface}"'},"​")],-1)),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.AbstractDimTable",href:"#DimensionalData.AbstractDimTable"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimTable")],-1)),s[58]||(s[58]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[59]||(s[59]=l('
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
',3))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.DimTable",href:"#DimensionalData.DimTable"},[i("span",{class:"jlbinding"},"DimensionalData.DimTable")],-1)),s[61]||(s[61]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[62]||(s[62]=l(`
julia
DimTable <: AbstractDimTable
 
 DimTable(s::AbstractDimStack; mergedims=nothing)
 DimTable(x::AbstractDimArray; layersfrom=nothing, mergedims=nothing)
@@ -272,7 +272,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0     1.0  1.0  1.0  1.0  1.0  1.0  1.0
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  …  1.0  1.0  1.0  1.0  1.0  1.0  1.0
 
-julia>
source
`,10))]),s[172]||(s[172]=i("h1",{id:"Group-by-methods",tabindex:"-1"},[a("Group by methods "),i("a",{class:"header-anchor",href:"#Group-by-methods","aria-label":'Permalink to "Group by methods {#Group-by-methods}"'},"​")],-1)),s[173]||(s[173]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DataAPI.groupby",href:"#DataAPI.groupby"},[i("span",{class:"jlbinding"},"DataAPI.groupby")],-1)),s[64]||(s[64]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=l(`
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
+julia>
source
`,10))]),s[172]||(s[172]=i("h1",{id:"Group-by-methods",tabindex:"-1"},[a("Group by methods "),i("a",{class:"header-anchor",href:"#Group-by-methods","aria-label":'Permalink to "Group by methods {#Group-by-methods}"'},"​")],-1)),s[173]||(s[173]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DataAPI.groupby",href:"#DataAPI.groupby"},[i("span",{class:"jlbinding"},"DataAPI.groupby")],-1)),s[64]||(s[64]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=l(`
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
 groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Dimension{<:Callable}...)
Group A by grouping functions or Bins over multiple dimensions.
Arguments
  • A: any AbstractDimArray or AbstractDimStack.
  • dims: Pairs such as groups = groupby(A, :dimname => groupingfunction) or wrapped Dimensions like groups = groupby(A, DimType(groupingfunction)). Instead of a grouping function Bins can be used to specify group bins.
Return value
A DimGroupByArray is returned, which is basically a regular AbstractDimArray but holding the grouped AbstractDimArray or AbstractDimStack. Its dims hold the sorted values returned by the grouping function/s.
Base julia and package methods work on DimGroupByArray as for any other AbstractArray of AbstractArray.
It is common to broadcast or map a reducing function over groups, such as mean or sum, like mean.(groups) or map(mean, groups). This will return a regular DimArray, or DimGroupByArray if dims keyword is used in the reducing function or it otherwise returns an AbstractDimArray or AbstractDimStack.
Example
Group some data along the time dimension:
julia
julia> using DimensionalData, Dates
 
 julia> A = rand(X(1:0.1:20), Y(1:20), Ti(DateTime(2000):Day(3):DateTime(2003)));
@@ -325,8 +325,8 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ⋮
  10        0.501105     0.500644
  11        0.498606     0.498801
- 12        0.501643     0.499298
source
`,18))]),i("details",I,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.DimGroupByArray",href:"#DimensionalData.DimGroupByArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimGroupByArray")],-1)),s[67]||(s[67]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[68]||(s[68]=l('
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
',5))]),i("details",O,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Bins",href:"#DimensionalData.Bins"},[i("span",{class:"jlbinding"},"DimensionalData.Bins")],-1)),s[70]||(s[70]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=l(`
julia
Bins(f, bins; labels, pad)
-Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
`,7))]),i("details",N,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.ranges",href:"#DimensionalData.ranges"},[i("span",{class:"jlbinding"},"DimensionalData.ranges")],-1)),s[73]||(s[73]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=l('
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",q,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.intervals",href:"#DimensionalData.intervals"},[i("span",{class:"jlbinding"},"DimensionalData.intervals")],-1)),s[76]||(s[76]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=l('
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",z,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.CyclicBins",href:"#DimensionalData.CyclicBins"},[i("span",{class:"jlbinding"},"DimensionalData.CyclicBins")],-1)),s[79]||(s[79]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=l('
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
',7))]),i("details",X,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.seasons",href:"#DimensionalData.seasons"},[i("span",{class:"jlbinding"},"DimensionalData.seasons")],-1)),s[82]||(s[82]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=l('
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
',5))]),i("details",Y,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.months",href:"#DimensionalData.months"},[i("span",{class:"jlbinding"},"DimensionalData.months")],-1)),s[85]||(s[85]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=l('
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
',6))]),i("details",P,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.hours",href:"#DimensionalData.hours"},[i("span",{class:"jlbinding"},"DimensionalData.hours")],-1)),s[88]||(s[88]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=l('
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
',6))]),s[174]||(s[174]=i("h1",{id:"Utility-methods",tabindex:"-1"},[a("Utility methods "),i("a",{class:"header-anchor",href:"#Utility-methods","aria-label":'Permalink to "Utility methods {#Utility-methods}"'},"​")],-1)),s[175]||(s[175]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",R,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.set",href:"#DimensionalData.Dimensions.Lookups.set"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.set")],-1)),s[91]||(s[91]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=l(`
julia
set(x, val)
+ 12        0.501643     0.499298
source
`,18))]),i("details",I,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.DimGroupByArray",href:"#DimensionalData.DimGroupByArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimGroupByArray")],-1)),s[67]||(s[67]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[68]||(s[68]=l('
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
',5))]),i("details",O,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Bins",href:"#DimensionalData.Bins"},[i("span",{class:"jlbinding"},"DimensionalData.Bins")],-1)),s[70]||(s[70]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=l(`
julia
Bins(f, bins; labels, pad)
+Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
`,7))]),i("details",N,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.ranges",href:"#DimensionalData.ranges"},[i("span",{class:"jlbinding"},"DimensionalData.ranges")],-1)),s[73]||(s[73]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=l('
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",q,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.intervals",href:"#DimensionalData.intervals"},[i("span",{class:"jlbinding"},"DimensionalData.intervals")],-1)),s[76]||(s[76]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=l('
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",z,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.CyclicBins",href:"#DimensionalData.CyclicBins"},[i("span",{class:"jlbinding"},"DimensionalData.CyclicBins")],-1)),s[79]||(s[79]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=l('
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
',7))]),i("details",X,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.seasons",href:"#DimensionalData.seasons"},[i("span",{class:"jlbinding"},"DimensionalData.seasons")],-1)),s[82]||(s[82]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=l('
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
',5))]),i("details",Y,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.months",href:"#DimensionalData.months"},[i("span",{class:"jlbinding"},"DimensionalData.months")],-1)),s[85]||(s[85]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=l('
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
',6))]),i("details",P,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.hours",href:"#DimensionalData.hours"},[i("span",{class:"jlbinding"},"DimensionalData.hours")],-1)),s[88]||(s[88]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=l('
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
',6))]),s[174]||(s[174]=i("h1",{id:"Utility-methods",tabindex:"-1"},[a("Utility methods "),i("a",{class:"header-anchor",href:"#Utility-methods","aria-label":'Permalink to "Utility methods {#Utility-methods}"'},"​")],-1)),s[175]||(s[175]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",R,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.set",href:"#DimensionalData.Dimensions.Lookups.set"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.set")],-1)),s[91]||(s[91]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=l(`
julia
set(x, val)
 set(x, args::Pairs...) => x with updated field/s
 set(x, args...; kw...) => x with updated field/s
 set(x, args::Tuple{Vararg{Dimension}}; kw...) => x with updated field/s
@@ -387,11 +387,11 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ →  -20.0  -10.0  0.0  10.0
  10.0    0.0    0.0  0.0   0.0
  20.0    0.0    0.0  0.0   0.0
- 30.0    0.0    0.0  0.0   0.0
source
`,20))]),i("details",G,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.rebuild",href:"#DimensionalData.Dimensions.Lookups.rebuild"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.rebuild")],-1)),s[94]||(s[94]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=l('
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
',18))]),i("details",V,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.modify",href:"#DimensionalData.modify"},[i("span",{class:"jlbinding"},"DimensionalData.modify")],-1)),s[97]||(s[97]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=l(`
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
+ 30.0    0.0    0.0  0.0   0.0
source
`,20))]),i("details",G,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.rebuild",href:"#DimensionalData.Dimensions.Lookups.rebuild"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.rebuild")],-1)),s[94]||(s[94]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=l('
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
',18))]),i("details",V,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.modify",href:"#DimensionalData.modify"},[i("span",{class:"jlbinding"},"DimensionalData.modify")],-1)),s[97]||(s[97]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=l(`
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
 modify(f, s::AbstractDimStack) => AbstractDimStack
 modify(f, dim::Dimension) => Dimension
 modify(f, x, lookupdim::Dimension) => typeof(x)
Modify the parent data, rebuilding the object wrapper without change. f must return a AbstractArray of the same size as the original.
This method is mostly useful as a way of swapping the parent array type of an object.
Example
If we have a previously-defined DimArray, we can copy it to an Nvidia GPU with:
julia
A = DimArray(rand(100, 100), (X, Y))
-modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
`,8))]),i("details",M,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.@d",href:"#DimensionalData.@d"},[i("span",{class:"jlbinding"},"DimensionalData.@d")],-1)),s[100]||(s[100]=a()),t(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[101]||(s[101]=l(`
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
+modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
`,8))]),i("details",M,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.@d",href:"#DimensionalData.@d"},[i("span",{class:"jlbinding"},"DimensionalData.@d")],-1)),s[100]||(s[100]=a()),t(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[101]||(s[101]=l(`
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
 da1 = ones(X(3))
 da2 = fill(2, Y(4), X(3))
 
@@ -403,7 +403,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 
 @d @. da1 * da2
 # Use parentheses areound \`@.\` if you need to pass options
-@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
`,13))]),i("details",U,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.broadcast_dims",href:"#DimensionalData.broadcast_dims"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims")],-1)),s[103]||(s[103]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=l('
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
',6))]),i("details",Z,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.broadcast_dims!",href:"#DimensionalData.broadcast_dims!"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims!")],-1)),s[106]||(s[106]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=l('
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
',6))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.mergedims",href:"#DimensionalData.mergedims"},[i("span",{class:"jlbinding"},"DimensionalData.mergedims")],-1)),s[109]||(s[109]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[110]||(s[110]=l(`
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
+@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
`,13))]),i("details",U,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.broadcast_dims",href:"#DimensionalData.broadcast_dims"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims")],-1)),s[103]||(s[103]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=l('
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
',6))]),i("details",Z,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.broadcast_dims!",href:"#DimensionalData.broadcast_dims!"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims!")],-1)),s[106]||(s[106]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=l('
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
',6))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.mergedims",href:"#DimensionalData.mergedims"},[i("span",{class:"jlbinding"},"DimensionalData.mergedims")],-1)),s[109]||(s[109]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[110]||(s[110]=l(`
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
 
 julia> ds = (X(0:0.1:0.4), Y(10:10:100), Ti([0, 3, 4]))
 (↓ X  0.0:0.1:0.4,
@@ -412,9 +412,9 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 
 julia> mergedims(ds, (X, Y) => :space)
 (↓ Ti    [0, 3, 4],
-→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
-mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
`,11))]),i("details",W,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.unmergedims",href:"#DimensionalData.unmergedims"},[i("span",{class:"jlbinding"},"DimensionalData.unmergedims")],-1)),s[112]||(s[112]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[113]||(s[113]=l(`
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
-unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
`,6))]),i("details",J,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.reorder",href:"#DimensionalData.reorder"},[i("span",{class:"jlbinding"},"DimensionalData.reorder")],-1)),s[115]||(s[115]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[116]||(s[116]=l(`
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
+→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
+mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
`,11))]),i("details",W,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.unmergedims",href:"#DimensionalData.unmergedims"},[i("span",{class:"jlbinding"},"DimensionalData.unmergedims")],-1)),s[112]||(s[112]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[113]||(s[113]=l(`
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
+unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
`,6))]),i("details",J,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.reorder",href:"#DimensionalData.reorder"},[i("span",{class:"jlbinding"},"DimensionalData.reorder")],-1)),s[115]||(s[115]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[116]||(s[116]=l(`
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
 reorder(A::Union{AbstractDimArray,AbstractDimStack}, order)
 reorder(A::Dimension, order::Order)
Reorder every dims index/array to order, or reorder index for the given dimension(s) in order.
order can be an Order, Dimension => Order pairs. A Tuple of Dimensions or any object that defines dims can be used in which case the dimensions of this object are used for reordering.
If no axis reversal is required the same objects will be returned, without allocation.
Example
julia
using DimensionalData
 
@@ -428,7 +428,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 reorder(rev, da) == da
 
 # output
-true
source
`,7))]),s[176]||(s[176]=i("h1",{id:"Global-lookup-strictness-settings",tabindex:"-1"},[a("Global lookup strictness settings "),i("a",{class:"header-anchor",href:"#Global-lookup-strictness-settings","aria-label":'Permalink to "Global lookup strictness settings {#Global-lookup-strictness-settings}"'},"​")],-1)),s[177]||(s[177]=i("p",null,[a("Control how strict DimensionalData when comparing "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Lookup"},[i("code",null,"Lookup")]),a("s before doing broadcasts and matrix multipications.")],-1)),s[178]||(s[178]=i("p",null,[a("In some cases (especially "),i("code",null,"DimVector"),a(" and small "),i("code",null,"DimArray"),a(") checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.")],-1)),i("details",$,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.strict_broadcast",href:"#DimensionalData.strict_broadcast"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast")],-1)),s[118]||(s[118]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[119]||(s[119]=l('
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",H,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.strict_broadcast!",href:"#DimensionalData.strict_broadcast!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast!")],-1)),s[121]||(s[121]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[122]||(s[122]=l('
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",Q,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.strict_matmul",href:"#DimensionalData.strict_matmul"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul")],-1)),s[124]||(s[124]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[125]||(s[125]=l('
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.strict_matmul!",href:"#DimensionalData.strict_matmul!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul!")],-1)),s[127]||(s[127]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[128]||(s[128]=l('
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),s[179]||(s[179]=i("p",null,"Base methods",-1)),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"Base.cat",href:"#Base.cat"},[i("span",{class:"jlbinding"},"Base.cat")],-1)),s[130]||(s[130]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[131]||(s[131]=l('
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
',8))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"Base.copy!",href:"#Base.copy!"},[i("span",{class:"jlbinding"},"Base.copy!")],-1)),s[133]||(s[133]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[134]||(s[134]=l('
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
',12))]),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"Base.eachslice",href:"#Base.eachslice"},[i("span",{class:"jlbinding"},"Base.eachslice")],-1)),s[136]||(s[136]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[137]||(s[137]=l(`
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
+true
source
`,7))]),s[176]||(s[176]=i("h1",{id:"Global-lookup-strictness-settings",tabindex:"-1"},[a("Global lookup strictness settings "),i("a",{class:"header-anchor",href:"#Global-lookup-strictness-settings","aria-label":'Permalink to "Global lookup strictness settings {#Global-lookup-strictness-settings}"'},"​")],-1)),s[177]||(s[177]=i("p",null,[a("Control how strict DimensionalData when comparing "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Lookup"},[i("code",null,"Lookup")]),a("s before doing broadcasts and matrix multipications.")],-1)),s[178]||(s[178]=i("p",null,[a("In some cases (especially "),i("code",null,"DimVector"),a(" and small "),i("code",null,"DimArray"),a(") checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.")],-1)),i("details",$,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.strict_broadcast",href:"#DimensionalData.strict_broadcast"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast")],-1)),s[118]||(s[118]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[119]||(s[119]=l('
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",H,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.strict_broadcast!",href:"#DimensionalData.strict_broadcast!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast!")],-1)),s[121]||(s[121]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[122]||(s[122]=l('
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",Q,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.strict_matmul",href:"#DimensionalData.strict_matmul"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul")],-1)),s[124]||(s[124]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[125]||(s[125]=l('
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.strict_matmul!",href:"#DimensionalData.strict_matmul!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul!")],-1)),s[127]||(s[127]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[128]||(s[128]=l('
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),s[179]||(s[179]=i("p",null,"Base methods",-1)),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"Base.cat",href:"#Base.cat"},[i("span",{class:"jlbinding"},"Base.cat")],-1)),s[130]||(s[130]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[131]||(s[131]=l('
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
',8))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"Base.copy!",href:"#Base.copy!"},[i("span",{class:"jlbinding"},"Base.copy!")],-1)),s[133]||(s[133]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[134]||(s[134]=l('
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
',12))]),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"Base.eachslice",href:"#Base.eachslice"},[i("span",{class:"jlbinding"},"Base.eachslice")],-1)),s[136]||(s[136]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[137]||(s[137]=l(`
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
            x=DimArray(randn(2, 3, 4), (X([:x1, :x2]), Y(1:3), Z)),
            y=DimArray(randn(2, 3, 5), (X([:x1, :x2]), Y(1:3), Ti))
        ));
@@ -451,13 +451,13 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 ├──────────────────────────────────────────────── layers ┤
   :x eltype: Float64 dims: Y size: 3
   :y eltype: Float64 dims: Y, Ti size: 3×5
-└────────────────────────────────────────────────────────┘
source
`,10))]),s[180]||(s[180]=i("p",null,[a("Most base methods work as expected, using "),i("code",null,"Dimension"),a(" wherever a "),i("code",null,"dims"),a(" keyword is used. They are not all specifically documented here.")],-1)),s[181]||(s[181]=i("h2",{id:"name",tabindex:"-1"},[a("Name "),i("a",{class:"header-anchor",href:"#name","aria-label":'Permalink to "Name"'},"​")],-1)),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.AbstractName",href:"#DimensionalData.AbstractName"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractName")],-1)),s[139]||(s[139]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=l('
julia
AbstractName
Abstract supertype for name wrappers.
source
',3))]),i("details",ts,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Name",href:"#DimensionalData.Name"},[i("span",{class:"jlbinding"},"DimensionalData.Name")],-1)),s[142]||(s[142]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=l(`
julia
Name <: AbstractName
+└────────────────────────────────────────────────────────┘
source
`,10))]),s[180]||(s[180]=i("p",null,[a("Most base methods work as expected, using "),i("code",null,"Dimension"),a(" wherever a "),i("code",null,"dims"),a(" keyword is used. They are not all specifically documented here.")],-1)),s[181]||(s[181]=i("h2",{id:"name",tabindex:"-1"},[a("Name "),i("a",{class:"header-anchor",href:"#name","aria-label":'Permalink to "Name"'},"​")],-1)),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.AbstractName",href:"#DimensionalData.AbstractName"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractName")],-1)),s[139]||(s[139]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=l('
julia
AbstractName
Abstract supertype for name wrappers.
source
',3))]),i("details",ts,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Name",href:"#DimensionalData.Name"},[i("span",{class:"jlbinding"},"DimensionalData.Name")],-1)),s[142]||(s[142]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=l(`
julia
Name <: AbstractName
 
 Name(name::Union{Symbol,Name) => Name
-Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
`,3))]),i("details",ls,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.NoName",href:"#DimensionalData.NoName"},[i("span",{class:"jlbinding"},"DimensionalData.NoName")],-1)),s[145]||(s[145]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=l(`
julia
NoName <: AbstractName
+Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
`,3))]),i("details",ls,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.NoName",href:"#DimensionalData.NoName"},[i("span",{class:"jlbinding"},"DimensionalData.NoName")],-1)),s[145]||(s[145]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=l(`
julia
NoName <: AbstractName
 
-NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
`,3))]),s[182]||(s[182]=i("h2",{id:"Internal-interface",tabindex:"-1"},[a("Internal interface "),i("a",{class:"header-anchor",href:"#Internal-interface","aria-label":'Permalink to "Internal interface {#Internal-interface}"'},"​")],-1)),i("details",es,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.DimArrayInterface",href:"#DimensionalData.DimArrayInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimArrayInterface")],-1)),s[148]||(s[148]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=l('
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",hs,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.DimStackInterface",href:"#DimensionalData.DimStackInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimStackInterface")],-1)),s[151]||(s[151]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=l('
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",ks,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.rebuild_from_arrays",href:"#DimensionalData.rebuild_from_arrays"},[i("span",{class:"jlbinding"},"DimensionalData.rebuild_from_arrays")],-1)),s[154]||(s[154]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[155]||(s[155]=l('
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
',6))]),i("details",ps,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.show_main",href:"#DimensionalData.show_main"},[i("span",{class:"jlbinding"},"DimensionalData.show_main")],-1)),s[157]||(s[157]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[158]||(s[158]=l(`
julia
show_main(io::IO, mime, A::AbstractDimArray)
-show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
`,6))]),i("details",ds,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.show_after",href:"#DimensionalData.show_after"},[i("span",{class:"jlbinding"},"DimensionalData.show_after")],-1)),s[160]||(s[160]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[161]||(s[161]=l(`
julia
show_after(io::IO, mime, A::AbstractDimArray)
-show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
`,11))]),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.refdims_title",href:"#DimensionalData.refdims_title"},[i("span",{class:"jlbinding"},"DimensionalData.refdims_title")],-1)),s[163]||(s[163]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[164]||(s[164]=l(`
julia
refdims_title(A::AbstractDimArray)
+NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
`,3))]),s[182]||(s[182]=i("h2",{id:"Internal-interface",tabindex:"-1"},[a("Internal interface "),i("a",{class:"header-anchor",href:"#Internal-interface","aria-label":'Permalink to "Internal interface {#Internal-interface}"'},"​")],-1)),i("details",hs,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.DimArrayInterface",href:"#DimensionalData.DimArrayInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimArrayInterface")],-1)),s[148]||(s[148]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=l('
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",es,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.DimStackInterface",href:"#DimensionalData.DimStackInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimStackInterface")],-1)),s[151]||(s[151]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=l('
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",ks,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.rebuild_from_arrays",href:"#DimensionalData.rebuild_from_arrays"},[i("span",{class:"jlbinding"},"DimensionalData.rebuild_from_arrays")],-1)),s[154]||(s[154]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[155]||(s[155]=l('
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
',6))]),i("details",ps,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.show_main",href:"#DimensionalData.show_main"},[i("span",{class:"jlbinding"},"DimensionalData.show_main")],-1)),s[157]||(s[157]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[158]||(s[158]=l(`
julia
show_main(io::IO, mime, A::AbstractDimArray)
+show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
`,6))]),i("details",rs,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.show_after",href:"#DimensionalData.show_after"},[i("span",{class:"jlbinding"},"DimensionalData.show_after")],-1)),s[160]||(s[160]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[161]||(s[161]=l(`
julia
show_after(io::IO, mime, A::AbstractDimArray)
+show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
`,11))]),i("details",ds,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.refdims_title",href:"#DimensionalData.refdims_title"},[i("span",{class:"jlbinding"},"DimensionalData.refdims_title")],-1)),s[163]||(s[163]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[164]||(s[164]=l(`
julia
refdims_title(A::AbstractDimArray)
 refdims_title(refdims::Tuple)
-refdims_title(refdim::Dimension)
Generate a title string based on reference dimension values.
source
`,3))])])}const us=e(d,[["render",os]]);export{ms as __pageData,us as default};
+refdims_title(refdim::Dimension)

Generate a title string based on reference dimension values.

source

`,3))])])}const us=h(r,[["render",os]]);export{ms as __pageData,us as default}; diff --git a/dev/assets/api_reference.md.Cbzd03zL.lean.js b/dev/assets/api_reference.md.CbFETIiO.lean.js similarity index 98% rename from dev/assets/api_reference.md.Cbzd03zL.lean.js rename to dev/assets/api_reference.md.CbFETIiO.lean.js index ce337a054..b7f4d5607 100644 --- a/dev/assets/api_reference.md.Cbzd03zL.lean.js +++ b/dev/assets/api_reference.md.CbFETIiO.lean.js @@ -1,4 +1,4 @@ -import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framework.B8l2DGq3.js";const ms=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),d={name:"api/reference.md"},r={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""};function os(gs,s,Es,ys,cs,Fs){const n=k("Badge");return p(),h("div",null,[s[165]||(s[165]=i("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),i("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=i("h2",{id:"arrays",tabindex:"-1"},[a("Arrays "),i("a",{class:"header-anchor",href:"#arrays","aria-label":'Permalink to "Arrays"'},"​")],-1)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.AbstractBasicDimArray",href:"#DimensionalData.AbstractBasicDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractBasicDimArray")],-1)),s[1]||(s[1]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=l('
julia
AbstractBasicDimArray <: AbstractArray

The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension

Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.

source

',4))]),i("details",o,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.AbstractDimArray",href:"#DimensionalData.AbstractDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimArray")],-1)),s[4]||(s[4]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=l('
julia
AbstractDimArray <: AbstractBasicArray

Abstract supertype for all "dim" arrays.

These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.

parent must return the source array.

They should have metadata, name and refdims methods, although these are optional.

A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.

Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"

source

',8))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.DimArray",href:"#DimensionalData.DimArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimArray")],-1)),s[7]||(s[7]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=l(`
julia
DimArray <: AbstractDimArray
+import{_ as h,c as e,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framework.BAAK0EyE.js";const ms=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),r={name:"api/reference.md"},d={class:"jldocstring custom-block",open:""},o={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ns={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""},hs={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},ks={class:"jldocstring custom-block",open:""},ps={class:"jldocstring custom-block",open:""},rs={class:"jldocstring custom-block",open:""},ds={class:"jldocstring custom-block",open:""};function os(gs,s,Es,ys,cs,Fs){const n=k("Badge");return p(),e("div",null,[s[165]||(s[165]=i("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),i("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=i("h2",{id:"arrays",tabindex:"-1"},[a("Arrays "),i("a",{class:"header-anchor",href:"#arrays","aria-label":'Permalink to "Arrays"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"DimensionalData.AbstractBasicDimArray",href:"#DimensionalData.AbstractBasicDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractBasicDimArray")],-1)),s[1]||(s[1]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=l('
julia
AbstractBasicDimArray <: AbstractArray
The abstract supertype for all arrays with a dims method that returns a Tuple of Dimension
Only keyword rebuild is guaranteed to work with AbstractBasicDimArray.
source
',4))]),i("details",o,[i("summary",null,[s[3]||(s[3]=i("a",{id:"DimensionalData.AbstractDimArray",href:"#DimensionalData.AbstractDimArray"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimArray")],-1)),s[4]||(s[4]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[5]||(s[5]=l('
julia
AbstractDimArray <: AbstractBasicArray
Abstract supertype for all "dim" arrays.
These arrays return a Tuple of Dimension from a dims method, and can be rebuilt using rebuild.
parent must return the source array.
They should have metadata, name and refdims methods, although these are optional.
A rebuild method for AbstractDimArray must accept data, dims, refdims, name, metadata arguments.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
source
',8))]),i("details",g,[i("summary",null,[s[6]||(s[6]=i("a",{id:"DimensionalData.DimArray",href:"#DimensionalData.DimArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimArray")],-1)),s[7]||(s[7]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[8]||(s[8]=l(`
julia
DimArray <: AbstractDimArray
 
 DimArray(data, dims, refdims, name, metadata)
 DimArray(data, dims::Tuple; refdims=(), name=NoName(), metadata=NoMetadata())
The main concrete subtype of AbstractDimArray.
DimArray maintains and updates its Dimensions through transformations and moves dimensions to reference dimension refdims after reducing operations (like e.g. mean).
Arguments
  • data: An AbstractArray.
  • dims: A Tuple of Dimension
  • name: A string name for the array. Shows in plots and tables.
  • refdims: refence dimensions. Usually set programmatically to track past slices and reductions of dimension for labelling and reconstruction.
  • metadata: Dict or Metadata object, or NoMetadata()
Indexing can be done with all regular indices, or with Dimensions and/or Selectors.
Indexing AbstractDimArray with non-range AbstractArray has undefined effects on the Dimension index. Use forward-ordered arrays only"
Example:
julia
julia> using Dates, DimensionalData
@@ -27,7 +27,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  20  0.774092
  30  0.823656
  40  0.637077
- 50  0.692235
source
`,10))]),s[167]||(s[167]=i("p",null,[a("Shorthand "),i("code",null,"AbstractDimArray"),a(" constructors:")],-1)),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Base.fill",href:"#Base.fill"},[i("span",{class:"jlbinding"},"Base.fill")],-1)),s[10]||(s[10]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=l(`
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
+ 50  0.692235
source
`,10))]),s[167]||(s[167]=i("p",null,[a("Shorthand "),i("code",null,"AbstractDimArray"),a(" constructors:")],-1)),i("details",E,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Base.fill",href:"#Base.fill"},[i("span",{class:"jlbinding"},"Base.fill")],-1)),s[10]||(s[10]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[11]||(s[11]=l(`
julia
Base.fill(x, dims::Dimension...; kw...) => DimArray
 Base.fill(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray with a fill value of x.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData, Random; Random.seed!(123);
 
 julia> rand(Bool, X(2), Y(4))
@@ -37,7 +37,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ X, → Y
 └──────────────────────┘
  0  0  0  0
- 1  0  0  1
source
`,8))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Base.rand",href:"#Base.rand"},[i("span",{class:"jlbinding"},"Base.rand")],-1)),s[13]||(s[13]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=l(`
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
+ 1  0  0  1
source
`,8))]),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Base.rand",href:"#Base.rand"},[i("span",{class:"jlbinding"},"Base.rand")],-1)),s[13]||(s[13]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=l(`
julia
Base.rand(x, dims::Dimension...; kw...) => DimArray
 Base.rand(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
 Base.rand(r::AbstractRNG, x, dims::Dimension...; kw...) => DimArray
Create a DimArray of random values.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
@@ -61,7 +61,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0       150.0       200.0
   :a    0.443494    0.253849    0.867547
   :b    0.745673    0.334152    0.0802658
-  :c    0.512083    0.427328    0.311448
source
`,8))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Base.zeros",href:"#Base.zeros"},[i("span",{class:"jlbinding"},"Base.zeros")],-1)),s[16]||(s[16]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=l(`
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
+  :c    0.512083    0.427328    0.311448
source
`,8))]),i("details",c,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Base.zeros",href:"#Base.zeros"},[i("span",{class:"jlbinding"},"Base.zeros")],-1)),s[16]||(s[16]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=l(`
julia
Base.zeros(x, dims::Dimension...; kw...) => DimArray
 Base.zeros(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of zeros.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> zeros(Bool, X(2), Y(4))
@@ -83,7 +83,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0  150.0  200.0
   :a    0.0    0.0    0.0
   :b    0.0    0.0    0.0
-  :c    0.0    0.0    0.0
source
`,8))]),i("details",F,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Base.ones",href:"#Base.ones"},[i("span",{class:"jlbinding"},"Base.ones")],-1)),s[19]||(s[19]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=l(`
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
+  :c    0.0    0.0    0.0
source
`,8))]),i("details",F,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Base.ones",href:"#Base.ones"},[i("span",{class:"jlbinding"},"Base.ones")],-1)),s[19]||(s[19]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=l(`
julia
Base.ones(x, dims::Dimension...; kw...) => DimArray
 Base.ones(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray
Create a DimArray of ones.
There are two kinds of Dimension value acepted:
  • A Dimension holding an AbstractVector will set the dimension index to that AbstractVector, and detect the dimension lookup.
  • A Dimension holding an Integer will set the length of the axis, and set the dimension lookup to NoLookup.
Keywords are the same as for DimArray.
Example
julia
julia> using DimensionalData
 
 julia> ones(Bool, X(2), Y(4))
@@ -105,8 +105,8 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  ↓ →  100.0  150.0  200.0
   :a    1.0    1.0    1.0
   :b    1.0    1.0    1.0
-  :c    1.0    1.0    1.0
source
`,8))]),s[168]||(s[168]=i("p",null,"Functions for getting information from objects:",-1)),i("details",C,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.dims",href:"#DimensionalData.Dimensions.dims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[22]||(s[22]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[23]||(s[23]=l(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
+  :c    1.0    1.0    1.0
source
`,8))]),s[168]||(s[168]=i("p",null,"Functions for getting information from objects:",-1)),i("details",C,[i("summary",null,[s[21]||(s[21]=i("a",{id:"DimensionalData.Dimensions.dims",href:"#DimensionalData.Dimensions.dims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dims")],-1)),s[22]||(s[22]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[23]||(s[23]=l(`
julia
dims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+dims(x, dim) => Dimension
Return a tuple of Dimensions for an object, in the order that matches the axes or columns of the underlying data.
dims can be Dimension, Dimension types, or Symbols for Dim{Symbol}.
The default is to return nothing.
source
julia
dims(x, query) => Tuple{Vararg{Dimension}}
 dims(x, query...) => Tuple{Vararg{Dimension}}
Get the dimension(s) matching the type(s) of the query dimension.
Lookup can be an Int or an Dimension, or a tuple containing any combination of either.
Arguments
  • x: any object with a dims method, or a Tuple of Dimension.
  • query: Tuple or a single Dimension or Dimension Type.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
@@ -120,13 +120,13 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  1.0  1.0  1.0
 
 julia> dims(A, (X, Y))
-(↓ X, → Y)
source
`,13))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.refdims",href:"#DimensionalData.Dimensions.refdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.refdims")],-1)),s[25]||(s[25]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=l(`
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
-refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
`,5))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.metadata",href:"#DimensionalData.Dimensions.Lookups.metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.metadata")],-1)),s[28]||(s[28]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[29]||(s[29]=l(`
julia
metadata(x) => (object metadata)
+(↓ X, → Y)
source
`,13))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"DimensionalData.Dimensions.refdims",href:"#DimensionalData.Dimensions.refdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.refdims")],-1)),s[25]||(s[25]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=l(`
julia
refdims(x, [dims::Tuple]) => Tuple{Vararg{Dimension}}
+refdims(x, dim) => Dimension
Reference dimensions for an array that is a slice or view of another array with more dimensions.
slicedims(a, dims) returns a tuple containing the current new dimensions and the new reference dimensions. Refdims can be stored in a field or discarded, as it is mostly to give context to plots. Ignoring refdims will simply leave some captions empty.
The default is to return an empty Tuple ().
source
`,5))]),i("details",u,[i("summary",null,[s[27]||(s[27]=i("a",{id:"DimensionalData.Dimensions.Lookups.metadata",href:"#DimensionalData.Dimensions.Lookups.metadata"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.metadata")],-1)),s[28]||(s[28]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[29]||(s[29]=l(`
julia
metadata(x) => (object metadata)
 metadata(x, dims::Tuple)  => Tuple (Dimension metadata)
-metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",D,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.name",href:"#DimensionalData.Dimensions.name"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name")],-1)),s[31]||(s[31]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[32]||(s[32]=l(`
julia
name(x) => Symbol
+metadata(xs::Tuple) => Tuple
Returns the metadata for an object or for the specified dimension(s)
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",D,[i("summary",null,[s[30]||(s[30]=i("a",{id:"DimensionalData.Dimensions.name",href:"#DimensionalData.Dimensions.name"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.name")],-1)),s[31]||(s[31]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[32]||(s[32]=l(`
julia
name(x) => Symbol
 name(xs:Tuple) => NTuple{N,Symbol}
 name(x, dims::Tuple) => NTuple{N,Symbol}
-name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.otherdims",href:"#DimensionalData.Dimensions.otherdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[34]||(s[34]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[35]||(s[35]=l(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
+name(x, dim) => Symbol
Get the name of an array or Dimension, or a tuple of of either as a Symbol.
Second argument dims can be Dimensions, Dimension types, or Symbols for Dim{Symbol}.
source
`,4))]),i("details",b,[i("summary",null,[s[33]||(s[33]=i("a",{id:"DimensionalData.Dimensions.otherdims",href:"#DimensionalData.Dimensions.otherdims"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.otherdims")],-1)),s[34]||(s[34]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[35]||(s[35]=l(`
julia
otherdims(x, query) => Tuple{Vararg{Dimension,N}}
Get the dimensions of an object not in query.
Arguments
  • x: any object with a dims method, a Tuple of Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
A tuple holding the unmatched dimensions is always returned.
Example
julia
julia> using DimensionalData, DimensionalData.Dimensions
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
 
@@ -134,7 +134,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (↓ Y, → Z)
 
 julia> otherdims(A, (Y, Z))
-(↓ X)
source
`,8))]),i("details",B,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.dimnum",href:"#DimensionalData.Dimensions.dimnum"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[37]||(s[37]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[38]||(s[38]=l(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
+(↓ X)
source
`,8))]),i("details",B,[i("summary",null,[s[36]||(s[36]=i("a",{id:"DimensionalData.Dimensions.dimnum",href:"#DimensionalData.Dimensions.dimnum"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.dimnum")],-1)),s[37]||(s[37]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[38]||(s[38]=l(`
julia
dimnum(x, query::Tuple) => NTuple{Int}
 dimnum(x, query) => Int
Get the number(s) of Dimension(s) as ordered in the dimensions of an object.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple, Array or single Dimension or dimension Type.
The return type will be a Tuple of Int or a single Int, depending on whether query is a Tuple or single Dimension.
Example
julia
julia> using DimensionalData
 
 julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
@@ -143,7 +143,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (3, 1, 2)
 
 julia> dimnum(A, Y)
-2
source
`,8))]),i("details",A,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.hasdim",href:"#DimensionalData.Dimensions.hasdim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[40]||(s[40]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[41]||(s[41]=l(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
+2
source
`,8))]),i("details",A,[i("summary",null,[s[39]||(s[39]=i("a",{id:"DimensionalData.Dimensions.hasdim",href:"#DimensionalData.Dimensions.hasdim"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.hasdim")],-1)),s[40]||(s[40]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[41]||(s[41]=l(`
julia
hasdim([f], x, query::Tuple) => NTuple{Bool}
 hasdim([f], x, query...) => NTuple{Bool}
 hasdim([f], x, query) => Bool
Check if an object x has dimensions that match or inherit from the query dimensions.
Arguments
  • x: any object with a dims method, a Tuple of Dimension or a single Dimension.
  • query: Tuple or single Dimension or dimension Type.
  • f: <: by default, but can be >: to match abstract types to concrete types.
Check if an object or tuple contains an Dimension, or a tuple of dimensions.
Example
julia
julia> using DimensionalData
 
@@ -156,7 +156,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (true, true, true)
 
 julia> hasdim(A, Ti)
-false
source
`,8))]),s[169]||(s[169]=i("h2",{id:"Multi-array-datasets",tabindex:"-1"},[a("Multi-array datasets "),i("a",{class:"header-anchor",href:"#Multi-array-datasets","aria-label":'Permalink to "Multi-array datasets {#Multi-array-datasets}"'},"​")],-1)),i("details",f,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.AbstractDimStack",href:"#DimensionalData.AbstractDimStack"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimStack")],-1)),s[43]||(s[43]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=l('
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
',10))]),i("details",j,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.DimStack",href:"#DimensionalData.DimStack"},[i("span",{class:"jlbinding"},"DimensionalData.DimStack")],-1)),s[46]||(s[46]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[47]||(s[47]=l(`
julia
DimStack <: AbstractDimStack
+false
source
`,8))]),s[169]||(s[169]=i("h2",{id:"Multi-array-datasets",tabindex:"-1"},[a("Multi-array datasets "),i("a",{class:"header-anchor",href:"#Multi-array-datasets","aria-label":'Permalink to "Multi-array datasets {#Multi-array-datasets}"'},"​")],-1)),i("details",f,[i("summary",null,[s[42]||(s[42]=i("a",{id:"DimensionalData.AbstractDimStack",href:"#DimensionalData.AbstractDimStack"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimStack")],-1)),s[43]||(s[43]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[44]||(s[44]=l('
julia
AbstractDimStack
Abstract supertype for dimensional stacks.
These have multiple layers of data, but share dimensions.
Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:
  • indexing with a Symbol as in dimstack[:symbol] returns a DimArray layer.
  • iteration and map apply over array layers, as indexed with a Symbol.
  • getindex and many base methods are applied as for DimArray - to avoid the need to always use map.
This design gives very succinct code when working with many-layered, mixed-dimension objects. But it may be jarring initially - the most surprising outcome is that dimstack[1] will return a NamedTuple of values for the first index in all layers, while first(dimstack) will return the first value of the iterator - the DimArray for the first layer.
See DimStack for the concrete implementation. Most methods are defined on the abstract type.
To extend AbstractDimStack, implement argument and keyword version of rebuild and also rebuild_from_arrays.
The constructor of an AbstractDimStack must accept a NamedTuple.
source
',10))]),i("details",j,[i("summary",null,[s[45]||(s[45]=i("a",{id:"DimensionalData.DimStack",href:"#DimensionalData.DimStack"},[i("span",{class:"jlbinding"},"DimensionalData.DimStack")],-1)),s[46]||(s[46]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[47]||(s[47]=l(`
julia
DimStack <: AbstractDimStack
 
 DimStack(data::AbstractDimArray...; kw...)
 DimStack(data::Tuple{Vararg{AbstractDimArray}}; kw...)
@@ -183,7 +183,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 (one = 4.0, two = 8.0, three = 12.0)
 
 julia> s[X(At(:a))] isa DimStack
-true
source
`,16))]),s[170]||(s[170]=i("h2",{id:"Dimension-generators",tabindex:"-1"},[a("Dimension generators "),i("a",{class:"header-anchor",href:"#Dimension-generators","aria-label":'Permalink to "Dimension generators {#Dimension-generators}"'},"​")],-1)),i("details",v,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.DimIndices",href:"#DimensionalData.DimIndices"},[i("span",{class:"jlbinding"},"DimensionalData.DimIndices")],-1)),s[49]||(s[49]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[50]||(s[50]=l(`
julia
DimIndices <: AbstractArray
+true
source
`,16))]),s[170]||(s[170]=i("h2",{id:"Dimension-generators",tabindex:"-1"},[a("Dimension generators "),i("a",{class:"header-anchor",href:"#Dimension-generators","aria-label":'Permalink to "Dimension generators {#Dimension-generators}"'},"​")],-1)),i("details",v,[i("summary",null,[s[48]||(s[48]=i("a",{id:"DimensionalData.DimIndices",href:"#DimensionalData.DimIndices"},[i("span",{class:"jlbinding"},"DimensionalData.DimIndices")],-1)),s[49]||(s[49]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[50]||(s[50]=l(`
julia
DimIndices <: AbstractArray
 
 DimIndices(x)
 DimIndices(dims::Tuple)
@@ -220,7 +220,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 └─────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'c'
  0.0  0.9063    0.0991336
- 0.6  0.745673  0.692209
source
`,9))]),i("details",w,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.DimSelectors",href:"#DimensionalData.DimSelectors"},[i("span",{class:"jlbinding"},"DimensionalData.DimSelectors")],-1)),s[52]||(s[52]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[53]||(s[53]=l(`
julia
DimSelectors <: AbstractArray
+ 0.6  0.745673  0.692209
source
`,9))]),i("details",w,[i("summary",null,[s[51]||(s[51]=i("a",{id:"DimensionalData.DimSelectors",href:"#DimensionalData.DimSelectors"},[i("span",{class:"jlbinding"},"DimensionalData.DimSelectors")],-1)),s[52]||(s[52]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[53]||(s[53]=l(`
julia
DimSelectors <: AbstractArray
 
 DimSelectors(x; selectors, atol...)
 DimSelectors(dims::Tuple; selectors, atol...)
@@ -238,11 +238,11 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ →  1.0        11.0       21.0
   1.0  0.691162    0.218579   0.539076
  10.0  0.0303789   0.420756   0.485687
- 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
`,10))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.DimPoints",href:"#DimensionalData.DimPoints"},[i("span",{class:"jlbinding"},"DimensionalData.DimPoints")],-1)),s[55]||(s[55]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l(`
julia
DimPoints <: AbstractArray
+ 22.0  0.0967863   0.864856   0.870485
Using At would make sure we only use exact interpolation, while Contains with sampling of Intervals would make sure that each values is taken only from an Interval that is present in the lookups.
source
`,10))]),i("details",x,[i("summary",null,[s[54]||(s[54]=i("a",{id:"DimensionalData.DimPoints",href:"#DimensionalData.DimPoints"},[i("span",{class:"jlbinding"},"DimensionalData.DimPoints")],-1)),s[55]||(s[55]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l(`
julia
DimPoints <: AbstractArray
 
 DimPoints(x; order)
 DimPoints(dims::Tuple; order)
-DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
`,6))]),s[171]||(s[171]=i("h2",{id:"Tables.jl/TableTraits.jl-interface",tabindex:"-1"},[a("Tables.jl/TableTraits.jl interface "),i("a",{class:"header-anchor",href:"#Tables.jl/TableTraits.jl-interface","aria-label":'Permalink to "Tables.jl/TableTraits.jl interface {#Tables.jl/TableTraits.jl-interface}"'},"​")],-1)),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.AbstractDimTable",href:"#DimensionalData.AbstractDimTable"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimTable")],-1)),s[58]||(s[58]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[59]||(s[59]=l('
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
',3))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.DimTable",href:"#DimensionalData.DimTable"},[i("span",{class:"jlbinding"},"DimensionalData.DimTable")],-1)),s[61]||(s[61]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[62]||(s[62]=l(`
julia
DimTable <: AbstractDimTable
+DimPoints(dims::Dimension; order)
Like CartesianIndices, but for the point values of the dimension index. Behaves as an Array of Tuple lookup values (whatever they are) for all combinations of the lookup values of dims.
Either a Dimension, a Tuple of Dimension or an object x that defines a dims method can be passed in.
Keywords
  • order: determines the order of the points, the same as the order of dims by default.
source
`,6))]),s[171]||(s[171]=i("h2",{id:"Tables.jl/TableTraits.jl-interface",tabindex:"-1"},[a("Tables.jl/TableTraits.jl interface "),i("a",{class:"header-anchor",href:"#Tables.jl/TableTraits.jl-interface","aria-label":'Permalink to "Tables.jl/TableTraits.jl interface {#Tables.jl/TableTraits.jl-interface}"'},"​")],-1)),i("details",T,[i("summary",null,[s[57]||(s[57]=i("a",{id:"DimensionalData.AbstractDimTable",href:"#DimensionalData.AbstractDimTable"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractDimTable")],-1)),s[58]||(s[58]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[59]||(s[59]=l('
julia
AbstractDimTable <: Tables.AbstractColumns
Abstract supertype for dim tables
source
',3))]),i("details",L,[i("summary",null,[s[60]||(s[60]=i("a",{id:"DimensionalData.DimTable",href:"#DimensionalData.DimTable"},[i("span",{class:"jlbinding"},"DimensionalData.DimTable")],-1)),s[61]||(s[61]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[62]||(s[62]=l(`
julia
DimTable <: AbstractDimTable
 
 DimTable(s::AbstractDimStack; mergedims=nothing)
 DimTable(x::AbstractDimArray; layersfrom=nothing, mergedims=nothing)
@@ -272,7 +272,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0     1.0  1.0  1.0  1.0  1.0  1.0  1.0
  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  …  1.0  1.0  1.0  1.0  1.0  1.0  1.0
 
-julia>
source
`,10))]),s[172]||(s[172]=i("h1",{id:"Group-by-methods",tabindex:"-1"},[a("Group by methods "),i("a",{class:"header-anchor",href:"#Group-by-methods","aria-label":'Permalink to "Group by methods {#Group-by-methods}"'},"​")],-1)),s[173]||(s[173]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DataAPI.groupby",href:"#DataAPI.groupby"},[i("span",{class:"jlbinding"},"DataAPI.groupby")],-1)),s[64]||(s[64]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=l(`
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
+julia>
source
`,10))]),s[172]||(s[172]=i("h1",{id:"Group-by-methods",tabindex:"-1"},[a("Group by methods "),i("a",{class:"header-anchor",href:"#Group-by-methods","aria-label":'Permalink to "Group by methods {#Group-by-methods}"'},"​")],-1)),s[173]||(s[173]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",S,[i("summary",null,[s[63]||(s[63]=i("a",{id:"DataAPI.groupby",href:"#DataAPI.groupby"},[i("span",{class:"jlbinding"},"DataAPI.groupby")],-1)),s[64]||(s[64]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[65]||(s[65]=l(`
julia
groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Pair...)
 groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Dimension{<:Callable}...)
Group A by grouping functions or Bins over multiple dimensions.
Arguments
  • A: any AbstractDimArray or AbstractDimStack.
  • dims: Pairs such as groups = groupby(A, :dimname => groupingfunction) or wrapped Dimensions like groups = groupby(A, DimType(groupingfunction)). Instead of a grouping function Bins can be used to specify group bins.
Return value
A DimGroupByArray is returned, which is basically a regular AbstractDimArray but holding the grouped AbstractDimArray or AbstractDimStack. Its dims hold the sorted values returned by the grouping function/s.
Base julia and package methods work on DimGroupByArray as for any other AbstractArray of AbstractArray.
It is common to broadcast or map a reducing function over groups, such as mean or sum, like mean.(groups) or map(mean, groups). This will return a regular DimArray, or DimGroupByArray if dims keyword is used in the reducing function or it otherwise returns an AbstractDimArray or AbstractDimStack.
Example
Group some data along the time dimension:
julia
julia> using DimensionalData, Dates
 
 julia> A = rand(X(1:0.1:20), Y(1:20), Ti(DateTime(2000):Day(3):DateTime(2003)));
@@ -325,8 +325,8 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ⋮
  10        0.501105     0.500644
  11        0.498606     0.498801
- 12        0.501643     0.499298
source
`,18))]),i("details",I,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.DimGroupByArray",href:"#DimensionalData.DimGroupByArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimGroupByArray")],-1)),s[67]||(s[67]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[68]||(s[68]=l('
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
',5))]),i("details",O,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Bins",href:"#DimensionalData.Bins"},[i("span",{class:"jlbinding"},"DimensionalData.Bins")],-1)),s[70]||(s[70]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=l(`
julia
Bins(f, bins; labels, pad)
-Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
`,7))]),i("details",N,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.ranges",href:"#DimensionalData.ranges"},[i("span",{class:"jlbinding"},"DimensionalData.ranges")],-1)),s[73]||(s[73]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=l('
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",q,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.intervals",href:"#DimensionalData.intervals"},[i("span",{class:"jlbinding"},"DimensionalData.intervals")],-1)),s[76]||(s[76]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=l('
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",z,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.CyclicBins",href:"#DimensionalData.CyclicBins"},[i("span",{class:"jlbinding"},"DimensionalData.CyclicBins")],-1)),s[79]||(s[79]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=l('
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
',7))]),i("details",X,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.seasons",href:"#DimensionalData.seasons"},[i("span",{class:"jlbinding"},"DimensionalData.seasons")],-1)),s[82]||(s[82]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=l('
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
',5))]),i("details",Y,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.months",href:"#DimensionalData.months"},[i("span",{class:"jlbinding"},"DimensionalData.months")],-1)),s[85]||(s[85]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=l('
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
',6))]),i("details",P,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.hours",href:"#DimensionalData.hours"},[i("span",{class:"jlbinding"},"DimensionalData.hours")],-1)),s[88]||(s[88]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=l('
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
',6))]),s[174]||(s[174]=i("h1",{id:"Utility-methods",tabindex:"-1"},[a("Utility methods "),i("a",{class:"header-anchor",href:"#Utility-methods","aria-label":'Permalink to "Utility methods {#Utility-methods}"'},"​")],-1)),s[175]||(s[175]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",R,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.set",href:"#DimensionalData.Dimensions.Lookups.set"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.set")],-1)),s[91]||(s[91]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=l(`
julia
set(x, val)
+ 12        0.501643     0.499298
source
`,18))]),i("details",I,[i("summary",null,[s[66]||(s[66]=i("a",{id:"DimensionalData.DimGroupByArray",href:"#DimensionalData.DimGroupByArray"},[i("span",{class:"jlbinding"},"DimensionalData.DimGroupByArray")],-1)),s[67]||(s[67]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[68]||(s[68]=l('
julia
DimGroupByArray <: AbstractDimArray
DimGroupByArray is essentially a DimArray but holding the results of a groupby operation.
Its dimensions are the sorted results of the grouping functions used in groupby.
This wrapper allows for specialisations on later broadcast or reducing operations, e.g. for chunk reading with DiskArrays.jl, because we know the data originates from a single array.
source
',5))]),i("details",O,[i("summary",null,[s[69]||(s[69]=i("a",{id:"DimensionalData.Bins",href:"#DimensionalData.Bins"},[i("span",{class:"jlbinding"},"DimensionalData.Bins")],-1)),s[70]||(s[70]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[71]||(s[71]=l(`
julia
Bins(f, bins; labels, pad)
+Bins(bins; labels, pad)
Specify bins to reduce groups after applying function f.
  • f: a grouping function of the lookup values, by default identity.
  • bins:
    • an Integer will divide the group values into equally spaced sections.
    • an AbstractArray of values will be treated as exact matches for the return value of f. For example, 1:3 will create 3 bins - 1, 2, 3.
    • an AbstractArray of IntervalSets.Interval can be used to explicitly define the intervals. Overlapping intervals have undefined behaviour.
Keywords
  • pad: fraction of the total interval to pad at each end when Bins contains an Integer. This avoids losing the edge values. Note this is a messy solution - it will often be prefereble to manually specify a Vector of chosen Intervals rather than relying on passing an Integer and pad.
  • labels: a list of descriptive labels for the bins. The labels need to have the same length as bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
`,7))]),i("details",N,[i("summary",null,[s[72]||(s[72]=i("a",{id:"DimensionalData.ranges",href:"#DimensionalData.ranges"},[i("span",{class:"jlbinding"},"DimensionalData.ranges")],-1)),s[73]||(s[73]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[74]||(s[74]=l('
julia
ranges(A::AbstractRange{<:Integer})
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",q,[i("summary",null,[s[75]||(s[75]=i("a",{id:"DimensionalData.intervals",href:"#DimensionalData.intervals"},[i("span",{class:"jlbinding"},"DimensionalData.intervals")],-1)),s[76]||(s[76]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[77]||(s[77]=l('
julia
intervals(A::AbstractRange)
Generate a Vector of UnitRange with length step(A)
source
',3))]),i("details",z,[i("summary",null,[s[78]||(s[78]=i("a",{id:"DimensionalData.CyclicBins",href:"#DimensionalData.CyclicBins"},[i("span",{class:"jlbinding"},"DimensionalData.CyclicBins")],-1)),s[79]||(s[79]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[80]||(s[80]=l('
julia
CyclicBins(f; cycle, start, step, labels)
Cyclic bins to reduce groups after applying function f. Groups can wrap around the cycle. This is used for grouping in seasons, months and hours but can also be used for custom cycles.
  • f: a grouping function of the lookup values, by default identity.
Keywords
  • cycle: the length of the cycle, in return values of f.
  • start: the start of the cycle: a return value of f.
  • step the number of sequential values to group.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected bins.
When the return value of f is a tuple, binning is applied to the last value of the tuples.
source
',7))]),i("details",X,[i("summary",null,[s[81]||(s[81]=i("a",{id:"DimensionalData.seasons",href:"#DimensionalData.seasons"},[i("span",{class:"jlbinding"},"DimensionalData.seasons")],-1)),s[82]||(s[82]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[83]||(s[83]=l('
julia
seasons(; [start=Dates.December, labels])
Generates CyclicBins for three month periods.
Keywords
  • start: By default seasons start in December, but any integer 1:12 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected quarters.
source
',5))]),i("details",Y,[i("summary",null,[s[84]||(s[84]=i("a",{id:"DimensionalData.months",href:"#DimensionalData.months"},[i("span",{class:"jlbinding"},"DimensionalData.months")],-1)),s[85]||(s[85]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[86]||(s[86]=l('
julia
months(step; [start=Dates.January, labels])
Generates CyclicBins for grouping to arbitrary month periods. These can wrap around the end of a year.
  • step the number of months to group.
Keywords
  • start: By default months start in January, but any integer 1:12 can be used.
  • labels: either a vector of labels matching the number of groups, or a function that generates labels from Vector{Int} of the selected months.
source
',6))]),i("details",P,[i("summary",null,[s[87]||(s[87]=i("a",{id:"DimensionalData.hours",href:"#DimensionalData.hours"},[i("span",{class:"jlbinding"},"DimensionalData.hours")],-1)),s[88]||(s[88]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[89]||(s[89]=l('
julia
hours(step; [start=0, labels])
Generates CyclicBins for grouping to arbitrary hour periods. These can wrap around the end of the day.
  • steps the number of hours to group.
Keywords
  • start: By default seasons start at 0, but any integer 1:24 can be used.
  • labels: either a vector of four labels, or a function that generates labels from Vector{Int} of the selected hours of the day.
source
',6))]),s[174]||(s[174]=i("h1",{id:"Utility-methods",tabindex:"-1"},[a("Utility methods "),i("a",{class:"header-anchor",href:"#Utility-methods","aria-label":'Permalink to "Utility methods {#Utility-methods}"'},"​")],-1)),s[175]||(s[175]=i("p",null,"For transforming DimensionalData objects:",-1)),i("details",R,[i("summary",null,[s[90]||(s[90]=i("a",{id:"DimensionalData.Dimensions.Lookups.set",href:"#DimensionalData.Dimensions.Lookups.set"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.set")],-1)),s[91]||(s[91]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[92]||(s[92]=l(`
julia
set(x, val)
 set(x, args::Pairs...) => x with updated field/s
 set(x, args...; kw...) => x with updated field/s
 set(x, args::Tuple{Vararg{Dimension}}; kw...) => x with updated field/s
@@ -387,11 +387,11 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
   ↓ →  -20.0  -10.0  0.0  10.0
  10.0    0.0    0.0  0.0   0.0
  20.0    0.0    0.0  0.0   0.0
- 30.0    0.0    0.0  0.0   0.0
source
`,20))]),i("details",G,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.rebuild",href:"#DimensionalData.Dimensions.Lookups.rebuild"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.rebuild")],-1)),s[94]||(s[94]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=l('
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
',18))]),i("details",V,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.modify",href:"#DimensionalData.modify"},[i("span",{class:"jlbinding"},"DimensionalData.modify")],-1)),s[97]||(s[97]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=l(`
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
+ 30.0    0.0    0.0  0.0   0.0
source
`,20))]),i("details",G,[i("summary",null,[s[93]||(s[93]=i("a",{id:"DimensionalData.Dimensions.Lookups.rebuild",href:"#DimensionalData.Dimensions.Lookups.rebuild"},[i("span",{class:"jlbinding"},"DimensionalData.Dimensions.Lookups.rebuild")],-1)),s[94]||(s[94]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[95]||(s[95]=l('
julia
rebuild(x; kw...)
Rebuild an object struct with updated field values.
x can be a AbstractDimArray, a Dimension, Lookup or other custom types.
This is an abstraction that allows inbuilt and custom types to be rebuilt to update their fields, as most objects in DimensionalData.jl are immutable.
Rebuild is mostly automated using ConstructionBase.setproperties. It should only be defined if your object has fields with with different names to DimensionalData objects. Try not to do that!
The arguments required are defined for the abstract type that has a rebuild method.
AbstractBasicDimArray:
  • dims: a Tuple of Dimension
AbstractDimArray:
  • data: the parent object - an AbstractArray
  • dims: a Tuple of Dimension
  • refdims: a Tuple of Dimension
  • name: A Symbol, or NoName and Name on GPU.
  • metadata: A Dict-like object
AbstractDimStack:
  • data: the parent object, often a NamedTuple
  • dims, refdims, metadata
Dimension:
  • val: anything.
Lookup:
  • data: the parent object, an AbstractArray
  • Note: argument rebuild is deprecated on AbstractDimArray and
AbstractDimStack in favour of always using the keyword version. In future the argument version will only be used on Dimension, which only have one argument.
source
',18))]),i("details",V,[i("summary",null,[s[96]||(s[96]=i("a",{id:"DimensionalData.modify",href:"#DimensionalData.modify"},[i("span",{class:"jlbinding"},"DimensionalData.modify")],-1)),s[97]||(s[97]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[98]||(s[98]=l(`
julia
modify(f, A::AbstractDimArray) => AbstractDimArray
 modify(f, s::AbstractDimStack) => AbstractDimStack
 modify(f, dim::Dimension) => Dimension
 modify(f, x, lookupdim::Dimension) => typeof(x)
Modify the parent data, rebuilding the object wrapper without change. f must return a AbstractArray of the same size as the original.
This method is mostly useful as a way of swapping the parent array type of an object.
Example
If we have a previously-defined DimArray, we can copy it to an Nvidia GPU with:
julia
A = DimArray(rand(100, 100), (X, Y))
-modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
`,8))]),i("details",M,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.@d",href:"#DimensionalData.@d"},[i("span",{class:"jlbinding"},"DimensionalData.@d")],-1)),s[100]||(s[100]=a()),t(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[101]||(s[101]=l(`
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
+modify(CuArray, A)
This also works for all the data layers in a DimStack.
source
`,8))]),i("details",M,[i("summary",null,[s[99]||(s[99]=i("a",{id:"DimensionalData.@d",href:"#DimensionalData.@d"},[i("span",{class:"jlbinding"},"DimensionalData.@d")],-1)),s[100]||(s[100]=a()),t(n,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[101]||(s[101]=l(`
julia
@d broadcast_expression options
Dimensional broadcast macro extending Base Julia broadcasting to work with missing and permuted dimensions.
Will permute and resshape singleton dimensions so that all AbstractDimArray in a broadcast will broadcast over matching dimensions.
It is possible to pass options as the second argument of the macro to control the behaviour, as a single assignment or as a NamedTuple. Options names must be written explicitly, not passed in namedtuple variable.
Options
  • dims: Pass a Tuple of Dimensions, Dimension types or Symbols to fix the dimension order of the output array. Otherwise dimensions will be in order of appearance. If dims with lookups are passed, these will be applied to the returned array with set.
  • strict: true or false. Check that all lookup values match explicitly.
All other keywords are passed to DimensionalData.rebuild. This means name, metadata, etc for the returned array can be set here, or for example missingval in Rasters.jl.
Example
julia
using DimensionalData
 da1 = ones(X(3))
 da2 = fill(2, Y(4), X(3))
 
@@ -403,7 +403,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 
 @d @. da1 * da2
 # Use parentheses areound \`@.\` if you need to pass options
-@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
`,13))]),i("details",U,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.broadcast_dims",href:"#DimensionalData.broadcast_dims"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims")],-1)),s[103]||(s[103]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=l('
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
',6))]),i("details",Z,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.broadcast_dims!",href:"#DimensionalData.broadcast_dims!"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims!")],-1)),s[106]||(s[106]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=l('
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
',6))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.mergedims",href:"#DimensionalData.mergedims"},[i("span",{class:"jlbinding"},"DimensionalData.mergedims")],-1)),s[109]||(s[109]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[110]||(s[110]=l(`
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
+@d (@. da1 * da2 .+ 5) dims=(Y, X)
source
`,13))]),i("details",U,[i("summary",null,[s[102]||(s[102]=i("a",{id:"DimensionalData.broadcast_dims",href:"#DimensionalData.broadcast_dims"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims")],-1)),s[103]||(s[103]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[104]||(s[104]=l('
julia
broadcast_dims(f, sources::AbstractDimArray...) => AbstractDimArray
Broadcast function f over the AbstractDimArrays in sources, permuting and reshaping dimensions to match where required. The result will contain all the dimensions in all passed in arrays in the order in which they are found.
Arguments
  • sources: AbstractDimArrays to broadcast over with f.
This is like broadcasting over every slice of A if it is sliced by the dimensions of B.
source
',6))]),i("details",Z,[i("summary",null,[s[105]||(s[105]=i("a",{id:"DimensionalData.broadcast_dims!",href:"#DimensionalData.broadcast_dims!"},[i("span",{class:"jlbinding"},"DimensionalData.broadcast_dims!")],-1)),s[106]||(s[106]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[107]||(s[107]=l('
julia
broadcast_dims!(f, dest::AbstractDimArray, sources::AbstractDimArray...) => dest
Broadcast function f over the AbstractDimArrays in sources, writing to dest. sources are permuting and reshaping dimensions to match where required.
The result will contain all the dimensions in all passed in arrays, in the order in which they are found.
Arguments
  • dest: AbstractDimArray to update.
  • sources: AbstractDimArrays to broadcast over with f.
source
',6))]),i("details",K,[i("summary",null,[s[108]||(s[108]=i("a",{id:"DimensionalData.mergedims",href:"#DimensionalData.mergedims"},[i("span",{class:"jlbinding"},"DimensionalData.mergedims")],-1)),s[109]||(s[109]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[110]||(s[110]=l(`
julia
mergedims(old_dims => new_dim) => Dimension
Return a dimension new_dim whose indices are a MergedLookup of the indices of old_dims.
source
julia
mergedims(dims, old_dims => new_dim, others::Pair...) => dims_new
If dimensions old_dims, new_dim, etc. are found in dims, then return new dims_new where all dims in old_dims have been combined into a single dim new_dim. The returned dimension will keep only the name of new_dim. Its coords will be a MergedLookup of the coords of the dims in old_dims. New dimensions are always placed at the end of dims_new. others contains other dimension pairs to be merged.
Example
julia
julia> using DimensionalData
 
 julia> ds = (X(0:0.1:0.4), Y(10:10:100), Ti([0, 3, 4]))
 (↓ X  0.0:0.1:0.4,
@@ -412,9 +412,9 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 
 julia> mergedims(ds, (X, Y) => :space)
 (↓ Ti    [0, 3, 4],
-→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
-mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
`,11))]),i("details",W,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.unmergedims",href:"#DimensionalData.unmergedims"},[i("span",{class:"jlbinding"},"DimensionalData.unmergedims")],-1)),s[112]||(s[112]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[113]||(s[113]=l(`
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
-unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
`,6))]),i("details",J,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.reorder",href:"#DimensionalData.reorder"},[i("span",{class:"jlbinding"},"DimensionalData.reorder")],-1)),s[115]||(s[115]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[116]||(s[116]=l(`
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
+→ space MergedLookup{Tuple{Float64, Int64}} [(0.0, 10), (0.1, 10), …, (0.3, 100), (0.4, 100)] (↓ X, → Y))
source
julia
mergedims(A::AbstractDimArray, dim_pairs::Pair...) => AbstractDimArray
+mergedims(A::AbstractDimStack, dim_pairs::Pair...) => AbstractDimStack
Return a new array or stack whose dimensions are the result of mergedims(dims(A), dim_pairs).
source
`,11))]),i("details",W,[i("summary",null,[s[111]||(s[111]=i("a",{id:"DimensionalData.unmergedims",href:"#DimensionalData.unmergedims"},[i("span",{class:"jlbinding"},"DimensionalData.unmergedims")],-1)),s[112]||(s[112]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[113]||(s[113]=l(`
julia
unmergedims(merged_dims::Tuple{Vararg{Dimension}}) => Tuple{Vararg{Dimension}}
Return the unmerged dimensions from a tuple of merged dimensions. However, the order of the original dimensions are not necessarily preserved.
source
julia
unmergedims(A::AbstractDimArray, original_dims) => AbstractDimArray
+unmergedims(A::AbstractDimStack, original_dims) => AbstractDimStack
Return a new array or stack whose dimensions are restored to their original prior to calling mergedims(A, dim_pairs).
source
`,6))]),i("details",J,[i("summary",null,[s[114]||(s[114]=i("a",{id:"DimensionalData.reorder",href:"#DimensionalData.reorder"},[i("span",{class:"jlbinding"},"DimensionalData.reorder")],-1)),s[115]||(s[115]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[116]||(s[116]=l(`
julia
reorder(A::Union{AbstractDimArray,AbstractDimStack}, order::Pair...)
 reorder(A::Union{AbstractDimArray,AbstractDimStack}, order)
 reorder(A::Dimension, order::Order)
Reorder every dims index/array to order, or reorder index for the given dimension(s) in order.
order can be an Order, Dimension => Order pairs. A Tuple of Dimensions or any object that defines dims can be used in which case the dimensions of this object are used for reordering.
If no axis reversal is required the same objects will be returned, without allocation.
Example
julia
using DimensionalData
 
@@ -428,7 +428,7 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 reorder(rev, da) == da
 
 # output
-true
source
`,7))]),s[176]||(s[176]=i("h1",{id:"Global-lookup-strictness-settings",tabindex:"-1"},[a("Global lookup strictness settings "),i("a",{class:"header-anchor",href:"#Global-lookup-strictness-settings","aria-label":'Permalink to "Global lookup strictness settings {#Global-lookup-strictness-settings}"'},"​")],-1)),s[177]||(s[177]=i("p",null,[a("Control how strict DimensionalData when comparing "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Lookup"},[i("code",null,"Lookup")]),a("s before doing broadcasts and matrix multipications.")],-1)),s[178]||(s[178]=i("p",null,[a("In some cases (especially "),i("code",null,"DimVector"),a(" and small "),i("code",null,"DimArray"),a(") checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.")],-1)),i("details",$,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.strict_broadcast",href:"#DimensionalData.strict_broadcast"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast")],-1)),s[118]||(s[118]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[119]||(s[119]=l('
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",H,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.strict_broadcast!",href:"#DimensionalData.strict_broadcast!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast!")],-1)),s[121]||(s[121]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[122]||(s[122]=l('
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",Q,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.strict_matmul",href:"#DimensionalData.strict_matmul"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul")],-1)),s[124]||(s[124]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[125]||(s[125]=l('
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.strict_matmul!",href:"#DimensionalData.strict_matmul!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul!")],-1)),s[127]||(s[127]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[128]||(s[128]=l('
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),s[179]||(s[179]=i("p",null,"Base methods",-1)),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"Base.cat",href:"#Base.cat"},[i("span",{class:"jlbinding"},"Base.cat")],-1)),s[130]||(s[130]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[131]||(s[131]=l('
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
',8))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"Base.copy!",href:"#Base.copy!"},[i("span",{class:"jlbinding"},"Base.copy!")],-1)),s[133]||(s[133]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[134]||(s[134]=l('
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
',12))]),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"Base.eachslice",href:"#Base.eachslice"},[i("span",{class:"jlbinding"},"Base.eachslice")],-1)),s[136]||(s[136]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[137]||(s[137]=l(`
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
+true
source
`,7))]),s[176]||(s[176]=i("h1",{id:"Global-lookup-strictness-settings",tabindex:"-1"},[a("Global lookup strictness settings "),i("a",{class:"header-anchor",href:"#Global-lookup-strictness-settings","aria-label":'Permalink to "Global lookup strictness settings {#Global-lookup-strictness-settings}"'},"​")],-1)),s[177]||(s[177]=i("p",null,[a("Control how strict DimensionalData when comparing "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Lookup"},[i("code",null,"Lookup")]),a("s before doing broadcasts and matrix multipications.")],-1)),s[178]||(s[178]=i("p",null,[a("In some cases (especially "),i("code",null,"DimVector"),a(" and small "),i("code",null,"DimArray"),a(") checking lookup values match may be too costly compared to the operations. You can turn check the current setting and turn them on or off with these methods.")],-1)),i("details",$,[i("summary",null,[s[117]||(s[117]=i("a",{id:"DimensionalData.strict_broadcast",href:"#DimensionalData.strict_broadcast"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast")],-1)),s[118]||(s[118]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[119]||(s[119]=l('
julia
strict_broadcast()
Check if strict broadcasting checks are active.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",H,[i("summary",null,[s[120]||(s[120]=i("a",{id:"DimensionalData.strict_broadcast!",href:"#DimensionalData.strict_broadcast!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_broadcast!")],-1)),s[121]||(s[121]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[122]||(s[122]=l('
julia
strict_broadcast!(x::Bool)
Set global broadcasting checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before brodcasting, to ensure that dimensions match closely.
An exception to this rule is when dimension are of length one, as these is ignored in broadcasts.
We always check that dimension names match in broadcasts. If you don't want this either, explicitly use parent(A) before broadcasting to remove the AbstractDimArray wrapper completely.
source
',6))]),i("details",Q,[i("summary",null,[s[123]||(s[123]=i("a",{id:"DimensionalData.strict_matmul",href:"#DimensionalData.strict_matmul"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul")],-1)),s[124]||(s[124]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[125]||(s[125]=l('
julia
strict_matmul()
Check if strickt broadcasting checks are active.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),i("details",_,[i("summary",null,[s[126]||(s[126]=i("a",{id:"DimensionalData.strict_matmul!",href:"#DimensionalData.strict_matmul!"},[i("span",{class:"jlbinding"},"DimensionalData.strict_matmul!")],-1)),s[127]||(s[127]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[128]||(s[128]=l('
julia
strict_matmul!(x::Bool)
Set global matrix multiplication checks to strict, or not for all AbstractDimArray.
With strict=true we check Lookup Order and values before attempting matrix multiplication, to ensure that dimensions match closely.
We always check that dimension names match in matrix multiplication. If you don't want this either, explicitly use parent(A) before multiplying to remove the AbstractDimArray wrapper completely.
source
',5))]),s[179]||(s[179]=i("p",null,"Base methods",-1)),i("details",ss,[i("summary",null,[s[129]||(s[129]=i("a",{id:"Base.cat",href:"#Base.cat"},[i("span",{class:"jlbinding"},"Base.cat")],-1)),s[130]||(s[130]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[131]||(s[131]=l('
julia
Base.cat(stacks::AbstractDimStack...; [keys=keys(stacks[1])], dims)
Concatenate all or a subset of layers for all passed in stacks.
Keywords
  • keys: Tuple of Symbol for the stack keys to concatenate.
  • dims: Dimension of child array to concatenate on.
Example
Concatenate the :sea_surface_temp and :humidity layers in the time dimension:
julia
cat(stacks...; keys=(:sea_surface_temp, :humidity), dims=Ti)
source
',8))]),i("details",is,[i("summary",null,[s[132]||(s[132]=i("a",{id:"Base.copy!",href:"#Base.copy!"},[i("span",{class:"jlbinding"},"Base.copy!")],-1)),s[133]||(s[133]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[134]||(s[134]=l('
julia
Base.copy!(dst::AbstractArray, src::AbstractDimStack, key::Key)
Copy the stack layer key to dst, which can be any AbstractArray.
Example
Copy the :humidity layer from stack to array.
julia
copy!(array, stack, :humidity)
source
julia
Base.copy!(dst::AbstractDimStack, src::AbstractDimStack, [keys=keys(dst)])
Copy all or a subset of layers from one stack to another.
Example
Copy just the :sea_surface_temp and :humidity layers from src to dst.
julia
copy!(dst::AbstractDimStack, src::AbstractDimStack, keys=(:sea_surface_temp, :humidity))
source
',12))]),i("details",as,[i("summary",null,[s[135]||(s[135]=i("a",{id:"Base.eachslice",href:"#Base.eachslice"},[i("span",{class:"jlbinding"},"Base.eachslice")],-1)),s[136]||(s[136]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[137]||(s[137]=l(`
julia
Base.eachslice(A::AbstractDimArray; dims,drop=true)
Create a generator that iterates over dimensions dims of A, returning arrays that select all the data from the other dimensions in A using views.
The generator has size and axes equivalent to those of the provided dims if drop=true. Otherwise it will have the same dimensionality as the underlying array with inner dimensions having size 1.
source
julia
Base.eachslice(stack::AbstractDimStack; dims, drop=true)
Create a generator that iterates over dimensions dims of stack, returning stacks that select all the data from the other dimensions in stack using views.
The generator has size and axes equivalent to those of the provided dims.
Examples
julia
julia> ds = DimStack((
            x=DimArray(randn(2, 3, 4), (X([:x1, :x2]), Y(1:3), Z)),
            y=DimArray(randn(2, 3, 5), (X([:x1, :x2]), Y(1:3), Ti))
        ));
@@ -451,13 +451,13 @@ import{_ as e,c as h,j as i,a,G as t,a4 as l,B as k,o as p}from"./chunks/framewo
 ├──────────────────────────────────────────────── layers ┤
   :x eltype: Float64 dims: Y size: 3
   :y eltype: Float64 dims: Y, Ti size: 3×5
-└────────────────────────────────────────────────────────┘
source
`,10))]),s[180]||(s[180]=i("p",null,[a("Most base methods work as expected, using "),i("code",null,"Dimension"),a(" wherever a "),i("code",null,"dims"),a(" keyword is used. They are not all specifically documented here.")],-1)),s[181]||(s[181]=i("h2",{id:"name",tabindex:"-1"},[a("Name "),i("a",{class:"header-anchor",href:"#name","aria-label":'Permalink to "Name"'},"​")],-1)),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.AbstractName",href:"#DimensionalData.AbstractName"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractName")],-1)),s[139]||(s[139]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=l('
julia
AbstractName
Abstract supertype for name wrappers.
source
',3))]),i("details",ts,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Name",href:"#DimensionalData.Name"},[i("span",{class:"jlbinding"},"DimensionalData.Name")],-1)),s[142]||(s[142]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=l(`
julia
Name <: AbstractName
+└────────────────────────────────────────────────────────┘
source
`,10))]),s[180]||(s[180]=i("p",null,[a("Most base methods work as expected, using "),i("code",null,"Dimension"),a(" wherever a "),i("code",null,"dims"),a(" keyword is used. They are not all specifically documented here.")],-1)),s[181]||(s[181]=i("h2",{id:"name",tabindex:"-1"},[a("Name "),i("a",{class:"header-anchor",href:"#name","aria-label":'Permalink to "Name"'},"​")],-1)),i("details",ns,[i("summary",null,[s[138]||(s[138]=i("a",{id:"DimensionalData.AbstractName",href:"#DimensionalData.AbstractName"},[i("span",{class:"jlbinding"},"DimensionalData.AbstractName")],-1)),s[139]||(s[139]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[140]||(s[140]=l('
julia
AbstractName
Abstract supertype for name wrappers.
source
',3))]),i("details",ts,[i("summary",null,[s[141]||(s[141]=i("a",{id:"DimensionalData.Name",href:"#DimensionalData.Name"},[i("span",{class:"jlbinding"},"DimensionalData.Name")],-1)),s[142]||(s[142]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[143]||(s[143]=l(`
julia
Name <: AbstractName
 
 Name(name::Union{Symbol,Name) => Name
-Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
`,3))]),i("details",ls,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.NoName",href:"#DimensionalData.NoName"},[i("span",{class:"jlbinding"},"DimensionalData.NoName")],-1)),s[145]||(s[145]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=l(`
julia
NoName <: AbstractName
+Name(name::NoName) => NoName
Name wrapper. This lets arrays keep symbol names when the array wrapper needs to be isbits, like for use on GPUs. It makes the name a property of the type. It's not necessary to use in normal use, a symbol is probably easier.
source
`,3))]),i("details",ls,[i("summary",null,[s[144]||(s[144]=i("a",{id:"DimensionalData.NoName",href:"#DimensionalData.NoName"},[i("span",{class:"jlbinding"},"DimensionalData.NoName")],-1)),s[145]||(s[145]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[146]||(s[146]=l(`
julia
NoName <: AbstractName
 
-NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
`,3))]),s[182]||(s[182]=i("h2",{id:"Internal-interface",tabindex:"-1"},[a("Internal interface "),i("a",{class:"header-anchor",href:"#Internal-interface","aria-label":'Permalink to "Internal interface {#Internal-interface}"'},"​")],-1)),i("details",es,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.DimArrayInterface",href:"#DimensionalData.DimArrayInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimArrayInterface")],-1)),s[148]||(s[148]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=l('
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",hs,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.DimStackInterface",href:"#DimensionalData.DimStackInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimStackInterface")],-1)),s[151]||(s[151]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=l('
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",ks,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.rebuild_from_arrays",href:"#DimensionalData.rebuild_from_arrays"},[i("span",{class:"jlbinding"},"DimensionalData.rebuild_from_arrays")],-1)),s[154]||(s[154]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[155]||(s[155]=l('
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
',6))]),i("details",ps,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.show_main",href:"#DimensionalData.show_main"},[i("span",{class:"jlbinding"},"DimensionalData.show_main")],-1)),s[157]||(s[157]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[158]||(s[158]=l(`
julia
show_main(io::IO, mime, A::AbstractDimArray)
-show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
`,6))]),i("details",ds,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.show_after",href:"#DimensionalData.show_after"},[i("span",{class:"jlbinding"},"DimensionalData.show_after")],-1)),s[160]||(s[160]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[161]||(s[161]=l(`
julia
show_after(io::IO, mime, A::AbstractDimArray)
-show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
`,11))]),i("details",rs,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.refdims_title",href:"#DimensionalData.refdims_title"},[i("span",{class:"jlbinding"},"DimensionalData.refdims_title")],-1)),s[163]||(s[163]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[164]||(s[164]=l(`
julia
refdims_title(A::AbstractDimArray)
+NoName()
NoName specifies an array is not named, and is the default name value for all AbstractDimArrays.
source
`,3))]),s[182]||(s[182]=i("h2",{id:"Internal-interface",tabindex:"-1"},[a("Internal interface "),i("a",{class:"header-anchor",href:"#Internal-interface","aria-label":'Permalink to "Internal interface {#Internal-interface}"'},"​")],-1)),i("details",hs,[i("summary",null,[s[147]||(s[147]=i("a",{id:"DimensionalData.DimArrayInterface",href:"#DimensionalData.DimArrayInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimArrayInterface")],-1)),s[148]||(s[148]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[149]||(s[149]=l('
julia
    DimArrayInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_parent_kw, :rebuild_dims_kw, :rebuild) and optional components (:refdims, :name, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches dimensions of array
  • size: length of dims matches dimensions of array
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • name:
    • rebuild updates name in arg rebuild
    • rebuild updates name in kw rebuild
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",es,[i("summary",null,[s[150]||(s[150]=i("a",{id:"DimensionalData.DimStackInterface",href:"#DimensionalData.DimStackInterface"},[i("span",{class:"jlbinding"},"DimensionalData.DimStackInterface")],-1)),s[151]||(s[151]=a()),t(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[152]||(s[152]=l('
julia
    DimStackInterface
An Interfaces.jl Interface with mandatory components (:dims, :refdims_base, :ndims, :size, :rebuild_parent, :rebuild_dims, :rebuild_layerdims, :rebuild_dims_kw, :rebuild_parent_kw, :rebuild_layerdims_kw, :rebuild) and optional components (:refdims, :metadata).
This is an early stage of inteface definition, many things are not yet tested.
Pass constructed AbstractDimArrays as test data.
They must not be zero dimensional, and should test at least 1, 2, and 3 dimensions.
Extended help
Mandatory keys:
  • dims:
    • defines a dims method
    • dims are updated on getindex
  • refdims_base: refdims returns a tuple of Dimension or empty
  • ndims: number of dims matches ndims of stack
  • size: length of dims matches size of stack
  • rebuild_parent: rebuild parent from args
  • rebuild_dims: rebuild paaarnet and dims from args
  • rebuild_layerdims: rebuild paaarnet and dims from args
  • rebuild_dims_kw: rebuild dims from args
  • rebuild_parent_kw: rebuild parent from args
  • rebuild_layerdims_kw: rebuild parent from args
  • rebuild: all rebuild arguments and keywords are accepted
Optional keys:
  • refdims:
    • refdims are updated in args rebuild
    • refdims are updated in kw rebuild
    • dropped dimensions are added to refdims
  • metadata:
    • rebuild updates metadata in arg rebuild
    • rebuild updates metadata in kw rebuild
source
',11))]),i("details",ks,[i("summary",null,[s[153]||(s[153]=i("a",{id:"DimensionalData.rebuild_from_arrays",href:"#DimensionalData.rebuild_from_arrays"},[i("span",{class:"jlbinding"},"DimensionalData.rebuild_from_arrays")],-1)),s[154]||(s[154]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[155]||(s[155]=l('
julia
rebuild_from_arrays(s::AbstractDimStack, das::NamedTuple{<:Any,<:Tuple{Vararg{AbstractDimArray}}}; kw...)
Rebuild an AbstractDimStack from a Tuple or NamedTuple of AbstractDimArray and an existing stack.
Keywords
Keywords are simply the fields of the stack object:
  • data
  • dims
  • refdims
  • metadata
  • layerdims
  • layermetadata
source
',6))]),i("details",ps,[i("summary",null,[s[156]||(s[156]=i("a",{id:"DimensionalData.show_main",href:"#DimensionalData.show_main"},[i("span",{class:"jlbinding"},"DimensionalData.show_main")],-1)),s[157]||(s[157]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[158]||(s[158]=l(`
julia
show_main(io::IO, mime, A::AbstractDimArray)
+show_main(io::IO, mime, A::AbstractDimStack)
Interface methods for adding the main part of show
At the least, you likely want to call:
julia
print_top(io, mime, A)
But read the DimensionalData.jl show.jl code for details.
source
`,6))]),i("details",rs,[i("summary",null,[s[159]||(s[159]=i("a",{id:"DimensionalData.show_after",href:"#DimensionalData.show_after"},[i("span",{class:"jlbinding"},"DimensionalData.show_after")],-1)),s[160]||(s[160]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[161]||(s[161]=l(`
julia
show_after(io::IO, mime, A::AbstractDimArray)
+show_after(io::IO, mime, A::AbstractDimStack)
Interface methods for adding additional show text for AbstractDimArray/AbstractDimStack subtypes.
Always include kw to avoid future breaking changes
Additional keywords may be added at any time.
blockwidth is passed in context
julia
blockwidth = get(io, :blockwidth, 10000)
Note - a ANSI box is left unclosed. This method needs to close it, or add more. blockwidth is the maximum length of the inner text.
Most likely you always want to at least close the show blocks with:
julia
print_block_close(io, blockwidth)
But read the DimensionalData.jl show.jl code for details.
source
`,11))]),i("details",ds,[i("summary",null,[s[162]||(s[162]=i("a",{id:"DimensionalData.refdims_title",href:"#DimensionalData.refdims_title"},[i("span",{class:"jlbinding"},"DimensionalData.refdims_title")],-1)),s[163]||(s[163]=a()),t(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[164]||(s[164]=l(`
julia
refdims_title(A::AbstractDimArray)
 refdims_title(refdims::Tuple)
-refdims_title(refdim::Dimension)
Generate a title string based on reference dimension values.
source
`,3))])])}const us=e(d,[["render",os]]);export{ms as __pageData,us as default};
+refdims_title(refdim::Dimension)

Generate a title string based on reference dimension values.

source

`,3))])])}const us=h(r,[["render",os]]);export{ms as __pageData,us as default}; diff --git a/dev/assets/app.DC0kJGHo.js b/dev/assets/app.DAF6qinH.js similarity index 90% rename from dev/assets/app.DC0kJGHo.js rename to dev/assets/app.DAF6qinH.js index ac0ba290c..75641df83 100644 --- a/dev/assets/app.DC0kJGHo.js +++ b/dev/assets/app.DAF6qinH.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.b8NutB4I.js";import{R as o,a5 as u,a6 as c,a7 as l,a8 as f,a9 as d,aa as m,ab as h,ac as g,ad as A,ae as v,d as P,u as R,v as w,s as y,af as C,ag as b,ah as E,ai as S}from"./chunks/framework.B8l2DGq3.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; +import{R as p}from"./chunks/theme.By8UT2IG.js";import{R as o,a5 as u,a6 as c,a7 as l,a8 as f,a9 as d,aa as m,ab as h,ac as g,ad as A,ae as v,d as P,u as R,v as w,s as y,af as C,ag as b,ah as E,ai as S}from"./chunks/framework.BAAK0EyE.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; diff --git a/dev/assets/basics.md.1otrM5SC.js b/dev/assets/basics.md.NNYczrui.js similarity index 73% rename from dev/assets/basics.md.1otrM5SC.js rename to dev/assets/basics.md.NNYczrui.js index 4705299d3..39e7485aa 100644 --- a/dev/assets/basics.md.1otrM5SC.js +++ b/dev/assets/basics.md.NNYczrui.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"basics.md","filePath":"basics.md","lastUpdated":null}'),l={name:"basics.md"};function k(t,s,e,p,d,r){return h(),a("div",null,s[0]||(s[0]=[n(`

Installation ​

If you want to use this package you need to install it first. You can do it using the following commands:

julia
julia> ] # ']' should be pressed
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BAAK0EyE.js";const y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"basics.md","filePath":"basics.md","lastUpdated":null}'),l={name:"basics.md"};function k(t,s,e,p,d,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Installation ​
If you want to use this package you need to install it first. You can do it using the following commands:
julia
julia> ] # ']' should be pressed
 pkg> add DimensionalData
or
julia
julia> using Pkg
 julia> Pkg.add("DimensionalData")
Additionally, it is recommended to check the version that you have installed with the status command.
julia
julia> ]
 pkg> status DimensionalData
Basics ​
Start using the package:
julia
using DimensionalData
and create your first DimArray
julia
julia> A = DimArray(rand(4,5), (a=1:4, b=1:5))
╭─────────────────────────╮
@@ -7,36 +7,36 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y
   ↓ a Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → b Sampled{Int64} 1:5 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- ↓ →  1         2          3         4         5
- 1    0.541806  0.0282382  0.762794  0.844547  0.919763
- 2    0.300768  0.247683   0.388243  0.95636   0.383632
- 3    0.70597   0.599558   0.803192  0.675435  0.487578
- 4    0.93479   0.624685   0.774731  0.216524  0.102405
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
+ ↓ →  1          2         3          4          5
+ 1    0.0333106  0.758203  0.973154   0.0710221  0.868064
+ 2    0.52849    0.571821  0.920182   0.545156   0.106593
+ 3    0.70366    0.539028  0.0670111  0.404635   0.799977
+ 4    0.68316    0.965008  0.0781957  0.130775   0.570914
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
 │ 10-element DimArray{Int8,1} │
 ├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ alpha Categorical{Char} 'a':1:'j' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
- 'a'  112
- 'b'   85
- 'c'  -86
- 'd'  123
- 'e'   95
- 'f'   59
- 'g'  -91
- 'h'   55
- 'i'  -78
- 'j'  -15
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
+ 'a'  -21
+ 'b'  101
+ 'c'   51
+ 'd'  -35
+ 'e'  106
+ 'f'   88
+ 'g'  -98
+ 'h'   14
+ 'i'  -73
+ 'j'  -54
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
 [:, :, 1] =
-  65  110   71  108  76  15  120   65  121  126
- 125   78  118  101  11  32   87  108   49  115
+ 46  76  38  32  120  122  29  57   9  23
+ 78  26   6  50   16  125  11  98  39  95
 
 [:, :, 2] =
- 106  84  74  80   9   26  35  74  28  47
-  87   1  42  54  15  107  77  23  89  34
+  50  101  46  48  76  17    1   69  11  112
+ 105   15  50  90   5  70  107  120  86   15
 
 [:, :, 3] =
- 65  66  113  1  123  16  69  104  -128  26
- 25  13   29  1   21  72  24   48    51  88
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
+ 38  59   41  47  41   11  117   82   74   61
+ 76  72  112  89  39  109   86  117  117  120
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
 │ 2×10×3 DimArray{Int8,3} │
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
   ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
@@ -44,6 +44,6 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y
   ↗ iter    Sampled{Int64} 1:3 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →        1    2    3    4   5   6    7    8    9   10
-  :left    65  110   71  108  76  15  120   65  121  126
-  :right  125   78  118  101  11  32   87  108   49  115
`,21)]))}const f=i(l,[["render",k]]);export{y as __pageData,f as default};
+ ↓ →       1   2   3   4    5    6   7   8   9  10
+  :left   46  76  38  32  120  122  29  57   9  23
+  :right  78  26   6  50   16  125  11  98  39  95
`,21)]))}const f=i(l,[["render",k]]);export{y as __pageData,f as default}; diff --git a/dev/assets/basics.md.1otrM5SC.lean.js b/dev/assets/basics.md.NNYczrui.lean.js similarity index 73% rename from dev/assets/basics.md.1otrM5SC.lean.js rename to dev/assets/basics.md.NNYczrui.lean.js index 4705299d3..39e7485aa 100644 --- a/dev/assets/basics.md.1otrM5SC.lean.js +++ b/dev/assets/basics.md.NNYczrui.lean.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"basics.md","filePath":"basics.md","lastUpdated":null}'),l={name:"basics.md"};function k(t,s,e,p,d,r){return h(),a("div",null,s[0]||(s[0]=[n(`

Installation ​

If you want to use this package you need to install it first. You can do it using the following commands:

julia
julia> ] # ']' should be pressed
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BAAK0EyE.js";const y=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"basics.md","filePath":"basics.md","lastUpdated":null}'),l={name:"basics.md"};function k(t,s,e,p,d,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Installation ​
If you want to use this package you need to install it first. You can do it using the following commands:
julia
julia> ] # ']' should be pressed
 pkg> add DimensionalData
or
julia
julia> using Pkg
 julia> Pkg.add("DimensionalData")
Additionally, it is recommended to check the version that you have installed with the status command.
julia
julia> ]
 pkg> status DimensionalData
Basics ​
Start using the package:
julia
using DimensionalData
and create your first DimArray
julia
julia> A = DimArray(rand(4,5), (a=1:4, b=1:5))
╭─────────────────────────╮
@@ -7,36 +7,36 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y
   ↓ a Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → b Sampled{Int64} 1:5 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- ↓ →  1         2          3         4         5
- 1    0.541806  0.0282382  0.762794  0.844547  0.919763
- 2    0.300768  0.247683   0.388243  0.95636   0.383632
- 3    0.70597   0.599558   0.803192  0.675435  0.487578
- 4    0.93479   0.624685   0.774731  0.216524  0.102405
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
+ ↓ →  1          2         3          4          5
+ 1    0.0333106  0.758203  0.973154   0.0710221  0.868064
+ 2    0.52849    0.571821  0.920182   0.545156   0.106593
+ 3    0.70366    0.539028  0.0670111  0.404635   0.799977
+ 4    0.68316    0.965008  0.0781957  0.130775   0.570914
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
 │ 10-element DimArray{Int8,1} │
 ├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ alpha Categorical{Char} 'a':1:'j' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
- 'a'  112
- 'b'   85
- 'c'  -86
- 'd'  123
- 'e'   95
- 'f'   59
- 'g'  -91
- 'h'   55
- 'i'  -78
- 'j'  -15
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
+ 'a'  -21
+ 'b'  101
+ 'c'   51
+ 'd'  -35
+ 'e'  106
+ 'f'   88
+ 'g'  -98
+ 'h'   14
+ 'i'  -73
+ 'j'  -54
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
 [:, :, 1] =
-  65  110   71  108  76  15  120   65  121  126
- 125   78  118  101  11  32   87  108   49  115
+ 46  76  38  32  120  122  29  57   9  23
+ 78  26   6  50   16  125  11  98  39  95
 
 [:, :, 2] =
- 106  84  74  80   9   26  35  74  28  47
-  87   1  42  54  15  107  77  23  89  34
+  50  101  46  48  76  17    1   69  11  112
+ 105   15  50  90   5  70  107  120  86   15
 
 [:, :, 3] =
- 65  66  113  1  123  16  69  104  -128  26
- 25  13   29  1   21  72  24   48    51  88
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
+ 38  59   41  47  41   11  117   82   74   61
+ 76  72  112  89  39  109   86  117  117  120
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
 │ 2×10×3 DimArray{Int8,3} │
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
   ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
@@ -44,6 +44,6 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const y
   ↗ iter    Sampled{Int64} 1:3 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →        1    2    3    4   5   6    7    8    9   10
-  :left    65  110   71  108  76  15  120   65  121  126
-  :right  125   78  118  101  11  32   87  108   49  115
`,21)]))}const f=i(l,[["render",k]]);export{y as __pageData,f as default};
+ ↓ →       1   2   3   4    5    6   7   8   9  10
+  :left   46  76  38  32  120  122  29  57   9  23
+  :right  78  26   6  50   16  125  11  98  39  95
`,21)]))}const f=i(l,[["render",k]]);export{y as __pageData,f as default}; diff --git a/dev/assets/broadcasts.md.CVzEqDHf.js b/dev/assets/broadcasts.md.Dz88oNfL.js similarity index 79% rename from dev/assets/broadcasts.md.CVzEqDHf.js rename to dev/assets/broadcasts.md.Dz88oNfL.js index 25f8fd840..e477dfcd8 100644 --- a/dev/assets/broadcasts.md.CVzEqDHf.js +++ b/dev/assets/broadcasts.md.Dz88oNfL.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g=JSON.parse('{"title":"Dimensional broadcasts with @d and broadcast_dims","description":"","frontmatter":{},"headers":[],"relativePath":"broadcasts.md","filePath":"broadcasts.md","lastUpdated":null}'),h={name:"broadcasts.md"};function l(k,s,t,p,d,f){return e(),a("div",null,s[0]||(s[0]=[n(`

Dimensional broadcasts with @d and broadcast_dims ​

Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with eachother, and that values match. Strict checks can be turned of globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).

The @d macro is a dimension-aware extension to regular dot brodcasting. broadcast_dims and broadcast_dims are analagous to Base julia broadcast.

Because we know the names of the dimensions, there is no ambiguity in which one we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.

As an added bonus, broadcast_dims even works on DimStacks. Currently @d does not work on DimStack.

Example: scaling along the time dimension ​

Define some dimensions:

julia
using DimensionalData
+import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BAAK0EyE.js";const g=JSON.parse('{"title":"Dimensional broadcasts with @d and broadcast_dims","description":"","frontmatter":{},"headers":[],"relativePath":"broadcasts.md","filePath":"broadcasts.md","lastUpdated":null}'),h={name:"broadcasts.md"};function t(l,s,k,p,d,f){return n(),a("div",null,s[0]||(s[0]=[e(`
Dimensional broadcasts with @d and broadcast_dims ​
Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with each other, and that values match. Strict checks can be turned off globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).
The @d macro is a dimension-aware extension to regular dot broadcasting. broadcast_dims is analogous to Base Julia's broadcast.
Because we know the names of the dimensions, there is no ambiguity in which ones we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.
As an added bonus, broadcast_dims even works on DimStacks. Currently, @d does not work on DimStack.
Example: scaling along the time dimension ​
Define some dimensions:
julia
using DimensionalData
 using Dates
 using Statistics
julia
julia> x, y, t = X(1:100), Y(1:25), Ti(DateTime(2000):Month(1):DateTime(2000, 12))
(↓ X  1:100,
 → Y  1:25,
@@ -26,16 +26,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size; got a dimension with lengths 100 and 12
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size: a has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(100), X{Sampled{Int64, UnitRange{Int64}, ForwardOrdered, Regular{Int64}, Points, NoMetadata}}([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100])) and b has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(12), Ti{Sampled{Dates.DateTime, StepRange{Dates.DateTime, Dates.Month}, ForwardOrdered, Regular{Dates.Month}, Points, NoMetadata}}([Dates.DateTime("2000-01-01T00:00:00"), Dates.DateTime("2000-02-01T00:00:00"), Dates.DateTime("2000-03-01T00:00:00"), Dates.DateTime("2000-04-01T00:00:00"), Dates.DateTime("2000-05-01T00:00:00"), Dates.DateTime("2000-06-01T00:00:00"), Dates.DateTime("2000-07-01T00:00:00"), Dates.DateTime("2000-08-01T00:00:00"), Dates.DateTime("2000-09-01T00:00:00"), Dates.DateTime("2000-10-01T00:00:00"), Dates.DateTime("2000-11-01T00:00:00"), Dates.DateTime("2000-12-01T00:00:00")]))
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -43,48 +43,48 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.49795
- 2000-03-01T00:00:00  0.493708
- 2000-04-01T00:00:00  0.492377
- 2000-05-01T00:00:00  0.496201
- 2000-06-01T00:00:00  0.496665
- 2000-07-01T00:00:00  0.489762
- 2000-08-01T00:00:00  0.503719
- 2000-09-01T00:00:00  0.505946
- 2000-10-01T00:00:00  0.499719
- 2000-11-01T00:00:00  0.490968
- 2000-12-01T00:00:00  0.501068
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.499674
+ 2000-03-01T00:00:00  0.510863
+ 2000-04-01T00:00:00  0.500818
+ 2000-05-01T00:00:00  0.501124
+ 2000-06-01T00:00:00  0.495215
+ 2000-07-01T00:00:00  0.489497
+ 2000-08-01T00:00:00  0.500495
+ 2000-09-01T00:00:00  0.491479
+ 2000-10-01T00:00:00  0.499282
+ 2000-11-01T00:00:00  0.491202
+ 2000-12-01T00:00:00  0.485718
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.995899
- 2000-03-01T00:00:00  1.48113
- 2000-04-01T00:00:00  1.96951
- 2000-05-01T00:00:00  2.48101
- 2000-06-01T00:00:00  2.97999
- 2000-07-01T00:00:00  3.42833
- 2000-08-01T00:00:00  4.02976
- 2000-09-01T00:00:00  4.55351
- 2000-10-01T00:00:00  4.99719
- 2000-11-01T00:00:00  5.40065
- 2000-12-01T00:00:00  6.01282
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.999348
+ 2000-03-01T00:00:00  1.53259
+ 2000-04-01T00:00:00  2.00327
+ 2000-05-01T00:00:00  2.50562
+ 2000-06-01T00:00:00  2.97129
+ 2000-07-01T00:00:00  3.42648
+ 2000-08-01T00:00:00  4.00396
+ 2000-09-01T00:00:00  4.42331
+ 2000-10-01T00:00:00  4.99282
+ 2000-11-01T00:00:00  5.40322
+ 2000-12-01T00:00:00  5.82862
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -92,16 +92,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -109,16 +109,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} scaled │
 ├──────────────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -126,13 +126,13 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483
`,36)]))}const y=i(h,[["render",l]]);export{g as __pageData,y as default};
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415
`,36)]))}const y=i(h,[["render",t]]);export{g as __pageData,y as default}; diff --git a/dev/assets/broadcasts.md.CVzEqDHf.lean.js b/dev/assets/broadcasts.md.Dz88oNfL.lean.js similarity index 79% rename from dev/assets/broadcasts.md.CVzEqDHf.lean.js rename to dev/assets/broadcasts.md.Dz88oNfL.lean.js index 25f8fd840..e477dfcd8 100644 --- a/dev/assets/broadcasts.md.CVzEqDHf.lean.js +++ b/dev/assets/broadcasts.md.Dz88oNfL.lean.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g=JSON.parse('{"title":"Dimensional broadcasts with @d and broadcast_dims","description":"","frontmatter":{},"headers":[],"relativePath":"broadcasts.md","filePath":"broadcasts.md","lastUpdated":null}'),h={name:"broadcasts.md"};function l(k,s,t,p,d,f){return e(),a("div",null,s[0]||(s[0]=[n(`

Dimensional broadcasts with @d and broadcast_dims ​

Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with eachother, and that values match. Strict checks can be turned of globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).

The @d macro is a dimension-aware extension to regular dot brodcasting. broadcast_dims and broadcast_dims are analagous to Base julia broadcast.

Because we know the names of the dimensions, there is no ambiguity in which one we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.

As an added bonus, broadcast_dims even works on DimStacks. Currently @d does not work on DimStack.

Example: scaling along the time dimension ​

Define some dimensions:

julia
using DimensionalData
+import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BAAK0EyE.js";const g=JSON.parse('{"title":"Dimensional broadcasts with @d and broadcast_dims","description":"","frontmatter":{},"headers":[],"relativePath":"broadcasts.md","filePath":"broadcasts.md","lastUpdated":null}'),h={name:"broadcasts.md"};function t(l,s,k,p,d,f){return n(),a("div",null,s[0]||(s[0]=[e(`
Dimensional broadcasts with @d and broadcast_dims ​
Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with each other, and that values match. Strict checks can be turned off globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).
The @d macro is a dimension-aware extension to regular dot broadcasting. broadcast_dims is analogous to Base Julia's broadcast.
Because we know the names of the dimensions, there is no ambiguity in which ones we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.
As an added bonus, broadcast_dims even works on DimStacks. Currently, @d does not work on DimStack.
Example: scaling along the time dimension ​
Define some dimensions:
julia
using DimensionalData
 using Dates
 using Statistics
julia
julia> x, y, t = X(1:100), Y(1:25), Ti(DateTime(2000):Month(1):DateTime(2000, 12))
(↓ X  1:100,
 → Y  1:25,
@@ -26,16 +26,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size; got a dimension with lengths 100 and 12
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size: a has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(100), X{Sampled{Int64, UnitRange{Int64}, ForwardOrdered, Regular{Int64}, Points, NoMetadata}}([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100])) and b has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(12), Ti{Sampled{Dates.DateTime, StepRange{Dates.DateTime, Dates.Month}, ForwardOrdered, Regular{Dates.Month}, Points, NoMetadata}}([Dates.DateTime("2000-01-01T00:00:00"), Dates.DateTime("2000-02-01T00:00:00"), Dates.DateTime("2000-03-01T00:00:00"), Dates.DateTime("2000-04-01T00:00:00"), Dates.DateTime("2000-05-01T00:00:00"), Dates.DateTime("2000-06-01T00:00:00"), Dates.DateTime("2000-07-01T00:00:00"), Dates.DateTime("2000-08-01T00:00:00"), Dates.DateTime("2000-09-01T00:00:00"), Dates.DateTime("2000-10-01T00:00:00"), Dates.DateTime("2000-11-01T00:00:00"), Dates.DateTime("2000-12-01T00:00:00")]))
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -43,48 +43,48 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.49795
- 2000-03-01T00:00:00  0.493708
- 2000-04-01T00:00:00  0.492377
- 2000-05-01T00:00:00  0.496201
- 2000-06-01T00:00:00  0.496665
- 2000-07-01T00:00:00  0.489762
- 2000-08-01T00:00:00  0.503719
- 2000-09-01T00:00:00  0.505946
- 2000-10-01T00:00:00  0.499719
- 2000-11-01T00:00:00  0.490968
- 2000-12-01T00:00:00  0.501068
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.499674
+ 2000-03-01T00:00:00  0.510863
+ 2000-04-01T00:00:00  0.500818
+ 2000-05-01T00:00:00  0.501124
+ 2000-06-01T00:00:00  0.495215
+ 2000-07-01T00:00:00  0.489497
+ 2000-08-01T00:00:00  0.500495
+ 2000-09-01T00:00:00  0.491479
+ 2000-10-01T00:00:00  0.499282
+ 2000-11-01T00:00:00  0.491202
+ 2000-12-01T00:00:00  0.485718
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.995899
- 2000-03-01T00:00:00  1.48113
- 2000-04-01T00:00:00  1.96951
- 2000-05-01T00:00:00  2.48101
- 2000-06-01T00:00:00  2.97999
- 2000-07-01T00:00:00  3.42833
- 2000-08-01T00:00:00  4.02976
- 2000-09-01T00:00:00  4.55351
- 2000-10-01T00:00:00  4.99719
- 2000-11-01T00:00:00  5.40065
- 2000-12-01T00:00:00  6.01282
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.999348
+ 2000-03-01T00:00:00  1.53259
+ 2000-04-01T00:00:00  2.00327
+ 2000-05-01T00:00:00  2.50562
+ 2000-06-01T00:00:00  2.97129
+ 2000-07-01T00:00:00  3.42648
+ 2000-08-01T00:00:00  4.00396
+ 2000-09-01T00:00:00  4.42331
+ 2000-10-01T00:00:00  4.99282
+ 2000-11-01T00:00:00  5.40322
+ 2000-12-01T00:00:00  5.82862
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -92,16 +92,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -109,16 +109,16 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} scaled │
 ├──────────────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -126,13 +126,13 @@ import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.B8l2DGq3.js";const g
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483
`,36)]))}const y=i(h,[["render",l]]);export{g as __pageData,y as default};
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415
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s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=De(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return 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similarity index 86% rename from dev/assets/cuda.md.DmbWVZki.js rename to dev/assets/cuda.md.D8Q-T0cP.js index e5a3dd9c8..85a3529d8 100644 --- a/dev/assets/cuda.md.DmbWVZki.js +++ b/dev/assets/cuda.md.D8Q-T0cP.js @@ -1,10 +1,10 @@ -import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.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
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BAAK0EyE.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
+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 ┐
@@ -25,4 +25,4 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const C
   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:
  1. Work seamlessly with Base julia broadcasts and other operations that already work on GPU.
  2. 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 converts 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 Name{:layer_name}() replaces :layer_name
  • Metadata dicts need to be stripped, 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};
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

GPU Integration goals ​

DimensionalData.jl has two GPU-related goals:

  1. Work seamlessly with Base Julia broadcasts and other operations that already work on GPU.

  2. 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/dev/assets/cuda.md.DmbWVZki.lean.js b/dev/assets/cuda.md.D8Q-T0cP.lean.js similarity index 86% rename from dev/assets/cuda.md.DmbWVZki.lean.js rename to dev/assets/cuda.md.D8Q-T0cP.lean.js index e5a3dd9c8..85a3529d8 100644 --- a/dev/assets/cuda.md.DmbWVZki.lean.js +++ b/dev/assets/cuda.md.D8Q-T0cP.lean.js @@ -1,10 +1,10 @@ -import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.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
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BAAK0EyE.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
+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 ┐
@@ -25,4 +25,4 @@ import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.B8l2DGq3.js";const C
   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:
  1. Work seamlessly with Base julia broadcasts and other operations that already work on GPU.
  2. 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 converts 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 Name{:layer_name}() replaces :layer_name
  • Metadata dicts need to be stripped, 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};
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

GPU Integration goals ​

DimensionalData.jl has two GPU-related goals:

  1. Work seamlessly with Base Julia broadcasts and other operations that already work on GPU.

  2. 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/dev/assets/dcljjmm.DHFgsALw.png b/dev/assets/dcljjmm.DHFgsALw.png new file mode 100644 index 000000000..f85d47902 Binary files /dev/null and b/dev/assets/dcljjmm.DHFgsALw.png differ diff --git a/dev/assets/dimarrays.md.DAoE3lud.js b/dev/assets/dimarrays.md.BoEU5ZVn.js similarity index 73% rename from dev/assets/dimarrays.md.DAoE3lud.js rename to dev/assets/dimarrays.md.BoEU5ZVn.js index 7379f4ce8..372b1b8c9 100644 --- a/dev/assets/dimarrays.md.DAoE3lud.js +++ b/dev/assets/dimarrays.md.BoEU5ZVn.js @@ -1,45 +1,45 @@ -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.B8l2DGq3.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),o={name:"dimarrays.md"};function r(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`

DimArrays ​

DimArrays 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
+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.BAAK0EyE.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),o={name:"dimarrays.md"};function r(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`
DimArrays ​
DimArrays 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}:
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
julia
julia> da = DimArray(A, (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
julia
julia> da = DimArray(A, (X, Y))
╭──────────────────────────╮
 │ 5×10 DimArray{Float64,2} │
 ├──────────────────────────┴─────────────────────────────── dims ┐
   ↓ X, → Y
 └────────────────────────────────────────────────────────────────┘
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
We can access a value with the same dimension wrappers:
julia
julia> da[Y(1), X(2)]
0.7076921858340348
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":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.760205  0.298179   0.545129   0.989571     0.554791  0.0697249  0.949105")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.553558  0.500157   0.295319   0.925873     0.386433  0.392848   0.26069")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.912065  0.917543   0.0965083  0.997672     0.492141  0.625296   0.391155")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.948512  0.0621546  0.167745   0.228964     0.90251   0.710967   0.885914")])])])],-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"}}," DimArray"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(A, (X, Y))")])])])],-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(`
+ 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)]
0.2236016853688918

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"}},", 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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"}}," fill"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"7"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"X"),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"}},"Y"),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":"#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"}},"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":"#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(` @@ -124,44 +124,44 @@ 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"./ch ├─────────────────────────┴──────────────────────────────── dims ┐ ↓ a, → b └────────────────────────────────────────────────────────────────┘ - 0.88841 0.329515 0.620055 0.20051 0.500642 - 0.284399 0.152765 0.465631 0.00337686 0.885929 - 0.0463176 0.143704 0.965831 0.754468 0.0684698 - 0.257822 0.411529 0.219583 0.6642 0.714286 - 0.706267 0.0450386 0.441735 0.137131 0.350422

and get a value, here another smaller DimArray:

julia
julia> da1[a=3, b=1:3]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.0463176
- 0.143704
- 0.965831

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)]
true

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))
╭────────────────────────────╮
+ 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)]
true

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]
- 0.0760848  0.0641884  0.680225  0.683696   0.0205228  0.563623  0.673966
- 0.0992684  0.970459   0.330777  0.241056   0.0196808  0.669021  0.107475
- 0.206406   0.444708   0.11996   0.322115   0.225865   0.802122  0.421692
- 0.94412    0.0192911  0.351413  0.468968   0.269685   0.150126  0.215458
- 0.917457   0.162553   0.184309  0.719495   0.221163   0.4231    0.721331
- 0.916702   0.794163   0.880796  0.839618   0.380161   0.180894  0.375182
- 0.48817    0.498764   0.904961  0.392377   0.625435   0.786147  0.769313
- 0.339888   0.358625   0.290734  0.0778416  0.451425   0.879774  0.605324
- 0.481184   0.828395   0.870276  0.0323182  0.195774   0.467484  0.255916
- 0.758865   0.680352   0.679221  0.920736   0.0375522  0.296639  0.139067
julia
julia> da2[(X(3), Z(5))]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.794017
- 0.865233
- 0.0540645
- 0.0474302
- 0.521021
- 0.836107
- 0.167104

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}}}:
+ 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)
julia
julia> da2[inds]
╭─────────────────────────╮
@@ -170,66 +170,66 @@ 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"./ch
   ↓ Y ,
   → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
 └──────────────────────────────────────────────────────────────────────────────┘
-  (3, 5)     (7, 4)    (8, 2)
- 0.794017   0.690695  0.89862
- 0.865233   0.992316  0.864765
- 0.0540645  0.409622  0.575698
- 0.0474302  0.133195  0.161393
- 0.521021   0.246162  0.337182
- 0.836107   0.365341  0.394703
- 0.167104   0.847018  0.213826

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)]
╭──────────────────────────╮
+  (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
 └────────────────────────────────────────────────────────────────┘
- 0.680225  0.457891   0.230047   0.634499  0.678022
- 0.330777  0.0275306  0.352138   0.739241  0.353691
- 0.11996   0.904604   0.708321   0.837335  0.0540645
- 0.351413  0.853553   0.934609   0.208365  0.803293
- 0.184309  0.91732    0.615764   0.526688  0.5573
- 0.880796  0.683091   0.761212   0.956031  0.3162
- 0.904961  0.589895   0.945371   0.409622  0.110998
- 0.290734  0.575698   0.497346   0.750906  0.354046
- 0.870276  0.320667   0.0559616  0.341835  0.234458
- 0.679221  0.950645   0.727554   0.306208  0.173513

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:

julia
julia> da1[X(3), 4]
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.
+ 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:

julia
julia> da1[X(3), 4]
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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422

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.
+ 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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422

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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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))
╭───────────────────────────╮
+ 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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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]
- 0.0610019  0.578989  0.760111  0.291577
- 0.980256   0.388907  0.711714  0.827852
- 0.225583   0.395594  0.433955  0.691887
julia
julia> sum(da5; dims=Ti)
╭───────────────────────────╮
+ 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]
- 1.31572  3.28438  1.73011  2.13035
- 2.5962   1.55745  2.5074   2.07151
- 2.31468  1.40687  3.06474  3.2858

Dims keywords

Methods where dims, dim types, or Symbols 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

Performance ​

Indexing with Dimensions has no runtime cost. Let's benchmark it:

julia
julia> using BenchmarkTools
+ 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 Symbols 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

Performance ​

Indexing with Dimensions has no runtime cost. Let's benchmark it:

julia
julia> using BenchmarkTools
 
 julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
 │ 3×3 DimArray{Float64,2} │
@@ -239,21 +239,21 @@ 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"./ch
  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):  2.785 ns … 18.735 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     2.795 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   2.822 ns ±  0.462 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 19.216 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.118 ns ±  0.327 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-                █                                            
-  ▇▃▁▁▁▁▁▁▁▁▁▁▁▁█▆▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
-  2.78 ns        Histogram: frequency by time        2.83 ns <
+                                █                            
+  ▃▁▆▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▆▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▃ ▂
+  3.1 ns         Histogram: frequency by time        3.12 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 … 17.072 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     3.105 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   3.136 ns ±  0.481 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 27.762 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.125 ns ±  0.396 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-  ▆█         ▆█           ▂          ▃                       ▂
-  ██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▄▅▁▁▁▁▁▁▁▁▁▄ █
-  3.1 ns       Histogram: log(frequency) by time     3.14 ns <
+  ▇█                                                      ▁  ▁
+  ██▆▁▃▁▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▅▅▆██ █
+  3.1 ns       Histogram: log(frequency) by time     3.67 ns <
 
  Memory estimate: 0 bytes, allocs estimate: 0.
`,52))])}const C=d(o,[["render",r]]);export{u as __pageData,C as default}; diff --git a/dev/assets/dimarrays.md.DAoE3lud.lean.js b/dev/assets/dimarrays.md.BoEU5ZVn.lean.js similarity index 73% rename from dev/assets/dimarrays.md.DAoE3lud.lean.js rename to dev/assets/dimarrays.md.BoEU5ZVn.lean.js index 7379f4ce8..372b1b8c9 100644 --- a/dev/assets/dimarrays.md.DAoE3lud.lean.js +++ b/dev/assets/dimarrays.md.BoEU5ZVn.lean.js @@ -1,45 +1,45 @@ -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.B8l2DGq3.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),o={name:"dimarrays.md"};function r(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`

DimArrays ​

DimArrays 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
+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.BAAK0EyE.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),o={name:"dimarrays.md"};function r(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`
DimArrays ​
DimArrays 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}:
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
julia
julia> da = DimArray(A, (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
julia
julia> da = DimArray(A, (X, Y))
╭──────────────────────────╮
 │ 5×10 DimArray{Float64,2} │
 ├──────────────────────────┴─────────────────────────────── dims ┐
   ↓ X, → Y
 └────────────────────────────────────────────────────────────────┘
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
We can access a value with the same dimension wrappers:
julia
julia> da[Y(1), X(2)]
0.7076921858340348
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":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.760205  0.298179   0.545129   0.989571     0.554791  0.0697249  0.949105")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.553558  0.500157   0.295319   0.925873     0.386433  0.392848   0.26069")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.912065  0.917543   0.0965083  0.997672     0.492141  0.625296   0.391155")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.948512  0.0621546  0.167745   0.228964     0.90251   0.710967   0.885914")])])])],-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"}}," DimArray"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(A, (X, Y))")])])])],-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(`
+ 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)]
0.2236016853688918

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"}},", 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"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Y"),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":"#959da5","--shiki-dark":"#959da5"}},"╭────────────────────────╮")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.934419 0.176197 0.675467 0.208384 … 0.273986 0.509231 0.299799")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.677974 0.468066 0.347379 0.541438 0.385262 0.52496 0.468184")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.0599255 0.247198 0.813229 0.391395 0.208542 0.750146 0.106224")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.557369 0.261741 0.44104 0.178902 0.561365 0.475747 0.264242")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.104867 0.497181 0.570254 0.906883 0.986207 0.0287126 0.108968")])])])],-1)])),_:1}),h(t,{label:"fill"},{default:e(()=>a[4]||(a[4]=[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"}}," fill"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"7"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"X"),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"}},"Y"),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":"#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"}},"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":"#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(` @@ -124,44 +124,44 @@ 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"./ch ├─────────────────────────┴──────────────────────────────── dims ┐ ↓ a, → b └────────────────────────────────────────────────────────────────┘ - 0.88841 0.329515 0.620055 0.20051 0.500642 - 0.284399 0.152765 0.465631 0.00337686 0.885929 - 0.0463176 0.143704 0.965831 0.754468 0.0684698 - 0.257822 0.411529 0.219583 0.6642 0.714286 - 0.706267 0.0450386 0.441735 0.137131 0.350422

and get a value, here another smaller DimArray:

julia
julia> da1[a=3, b=1:3]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.0463176
- 0.143704
- 0.965831

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)]
true

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))
╭────────────────────────────╮
+ 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)]
true

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]
- 0.0760848  0.0641884  0.680225  0.683696   0.0205228  0.563623  0.673966
- 0.0992684  0.970459   0.330777  0.241056   0.0196808  0.669021  0.107475
- 0.206406   0.444708   0.11996   0.322115   0.225865   0.802122  0.421692
- 0.94412    0.0192911  0.351413  0.468968   0.269685   0.150126  0.215458
- 0.917457   0.162553   0.184309  0.719495   0.221163   0.4231    0.721331
- 0.916702   0.794163   0.880796  0.839618   0.380161   0.180894  0.375182
- 0.48817    0.498764   0.904961  0.392377   0.625435   0.786147  0.769313
- 0.339888   0.358625   0.290734  0.0778416  0.451425   0.879774  0.605324
- 0.481184   0.828395   0.870276  0.0323182  0.195774   0.467484  0.255916
- 0.758865   0.680352   0.679221  0.920736   0.0375522  0.296639  0.139067
julia
julia> da2[(X(3), Z(5))]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.794017
- 0.865233
- 0.0540645
- 0.0474302
- 0.521021
- 0.836107
- 0.167104

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}}}:
+ 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)
julia
julia> da2[inds]
╭─────────────────────────╮
@@ -170,66 +170,66 @@ 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"./ch
   ↓ Y ,
   → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
 └──────────────────────────────────────────────────────────────────────────────┘
-  (3, 5)     (7, 4)    (8, 2)
- 0.794017   0.690695  0.89862
- 0.865233   0.992316  0.864765
- 0.0540645  0.409622  0.575698
- 0.0474302  0.133195  0.161393
- 0.521021   0.246162  0.337182
- 0.836107   0.365341  0.394703
- 0.167104   0.847018  0.213826

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)]
╭──────────────────────────╮
+  (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
 └────────────────────────────────────────────────────────────────┘
- 0.680225  0.457891   0.230047   0.634499  0.678022
- 0.330777  0.0275306  0.352138   0.739241  0.353691
- 0.11996   0.904604   0.708321   0.837335  0.0540645
- 0.351413  0.853553   0.934609   0.208365  0.803293
- 0.184309  0.91732    0.615764   0.526688  0.5573
- 0.880796  0.683091   0.761212   0.956031  0.3162
- 0.904961  0.589895   0.945371   0.409622  0.110998
- 0.290734  0.575698   0.497346   0.750906  0.354046
- 0.870276  0.320667   0.0559616  0.341835  0.234458
- 0.679221  0.950645   0.727554   0.306208  0.173513

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:

julia
julia> da1[X(3), 4]
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.
+ 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:

julia
julia> da1[X(3), 4]
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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422

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.
+ 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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422

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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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))
╭───────────────────────────╮
+ 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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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]
- 0.0610019  0.578989  0.760111  0.291577
- 0.980256   0.388907  0.711714  0.827852
- 0.225583   0.395594  0.433955  0.691887
julia
julia> sum(da5; dims=Ti)
╭───────────────────────────╮
+ 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]
- 1.31572  3.28438  1.73011  2.13035
- 2.5962   1.55745  2.5074   2.07151
- 2.31468  1.40687  3.06474  3.2858

Dims keywords

Methods where dims, dim types, or Symbols 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

Performance ​

Indexing with Dimensions has no runtime cost. Let's benchmark it:

julia
julia> using BenchmarkTools
+ 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 Symbols 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

Performance ​

Indexing with Dimensions has no runtime cost. Let's benchmark it:

julia
julia> using BenchmarkTools
 
 julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
 │ 3×3 DimArray{Float64,2} │
@@ -239,21 +239,21 @@ 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"./ch
  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):  2.785 ns … 18.735 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     2.795 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   2.822 ns ±  0.462 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 19.216 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.118 ns ±  0.327 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-                █                                            
-  ▇▃▁▁▁▁▁▁▁▁▁▁▁▁█▆▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
-  2.78 ns        Histogram: frequency by time        2.83 ns <
+                                █                            
+  ▃▁▆▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▆▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▃ ▂
+  3.1 ns         Histogram: frequency by time        3.12 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 … 17.072 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     3.105 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   3.136 ns ±  0.481 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 27.762 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.125 ns ±  0.396 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-  ▆█         ▆█           ▂          ▃                       ▂
-  ██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▄▅▁▁▁▁▁▁▁▁▁▄ █
-  3.1 ns       Histogram: log(frequency) by time     3.14 ns <
+  ▇█                                                      ▁  ▁
+  ██▆▁▃▁▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▅▅▆██ █
+  3.1 ns       Histogram: log(frequency) by time     3.67 ns <
 
  Memory estimate: 0 bytes, allocs estimate: 0.
`,52))])}const C=d(o,[["render",r]]);export{u as __pageData,C as default}; diff --git a/dev/assets/dimensions.md.Btc7hsPQ.js b/dev/assets/dimensions.md.Btc7hsPQ.js deleted file mode 100644 index e2f87a7e5..000000000 --- a/dev/assets/dimensions.md.Btc7hsPQ.js +++ /dev/null @@ -1,3 +0,0 @@ -import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.B8l2DGq3.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)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)

You can also make Dim dimensions with any name:

julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)

The wrapped value can be retrieved with val:

julia
julia> val(X(1))
1

DimensionalData.jl uses Dimensions everywhere:

  • Dimension are returned from dims to specify the names of the dimensions of an object

  • they wrap Lookups to associate the lookups with those names

  • to index into these objects, they wrap indices like Int or a Selector

This symmetry means we can ignore how data is organised, and label and access it by name, letting DD work out the details for us.

Dimensions are defined in the Dimensions submodule, 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/dev/assets/dimensions.md.Btc7hsPQ.lean.js b/dev/assets/dimensions.md.Btc7hsPQ.lean.js deleted file mode 100644 index e2f87a7e5..000000000 --- a/dev/assets/dimensions.md.Btc7hsPQ.lean.js +++ /dev/null @@ -1,3 +0,0 @@ -import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.B8l2DGq3.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)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)

You can also make Dim dimensions with any name:

julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)

The wrapped value can be retrieved with val:

julia
julia> val(X(1))
1

DimensionalData.jl uses Dimensions everywhere:

  • Dimension are returned from dims to specify the names of the dimensions of an object

  • they wrap Lookups to associate the lookups with those names

  • to index into these objects, they wrap indices like Int or a Selector

This symmetry means we can ignore how data is organised, and label and access it by name, letting DD work out the details for us.

Dimensions are defined in the Dimensions submodule, 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/dev/assets/dimensions.md.BukE2hG-.js b/dev/assets/dimensions.md.BukE2hG-.js new file mode 100644 index 000000000..034238f6c --- /dev/null +++ b/dev/assets/dimensions.md.BukE2hG-.js @@ -0,0 +1,3 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BAAK0EyE.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)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)

You can also create Dim dimensions with any name:

julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)

The wrapped value can be retrieved with val:

julia
julia> val(X(1))
1

DimensionalData.jl uses Dimensions everywhere:

  • Dimensions 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/dev/assets/dimensions.md.BukE2hG-.lean.js b/dev/assets/dimensions.md.BukE2hG-.lean.js new file mode 100644 index 000000000..034238f6c --- /dev/null +++ b/dev/assets/dimensions.md.BukE2hG-.lean.js @@ -0,0 +1,3 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BAAK0EyE.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)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)

You can also create Dim dimensions with any name:

julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)

The wrapped value can be retrieved with val:

julia
julia> val(X(1))
1

DimensionalData.jl uses Dimensions everywhere:

  • Dimensions 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/dev/assets/diskarrays.md.DNn9Ahrm.js b/dev/assets/diskarrays.md.DGsdRCSJ.js similarity index 54% rename from dev/assets/diskarrays.md.DNn9Ahrm.js rename to dev/assets/diskarrays.md.DGsdRCSJ.js index 0139c94ad..7e51e1423 100644 --- a/dev/assets/diskarrays.md.DNn9Ahrm.js +++ b/dev/assets/diskarrays.md.DGsdRCSJ.js @@ -1 +1 @@ -import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.B8l2DGq3.js";const m=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),s={name:"diskarrays.md"};function l(n,a,o,d,p,c){return e(),i("div",null,a[0]||(a[0]=[r('

DiskArrays.jl compatibility ​

DiskArrays.jl enables lazy, chunked application of:

  • broadcast

  • reductions

  • iteration

  • generators

  • zip

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.lj, GRIBDatasets.jl and CommonDataModel.jl

The combination of DiskArrays.jl and DimensionalData.jl is Julias answer to pythons xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.

They have no direct dependency relationships, with but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

',6)]))}const h=t(s,[["render",l]]);export{m as __pageData,h as default}; +import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.BAAK0EyE.js";const m=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),s={name:"diskarrays.md"};function l(n,a,o,d,p,c){return e(),i("div",null,a[0]||(a[0]=[r('

DiskArrays.jl compatibility ​

DiskArrays.jl enables lazy, chunked application of:

  • broadcast

  • reductions

  • iteration

  • generators

  • zip

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

The combination of DiskArrays.jl and DimensionalData.jl is Julia's answer to python's xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.

They have no direct dependency relationships, but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

',6)]))}const u=t(s,[["render",l]]);export{m as __pageData,u as default}; diff --git a/dev/assets/diskarrays.md.DNn9Ahrm.lean.js b/dev/assets/diskarrays.md.DGsdRCSJ.lean.js similarity index 54% rename from dev/assets/diskarrays.md.DNn9Ahrm.lean.js rename to dev/assets/diskarrays.md.DGsdRCSJ.lean.js index 0139c94ad..7e51e1423 100644 --- a/dev/assets/diskarrays.md.DNn9Ahrm.lean.js +++ b/dev/assets/diskarrays.md.DGsdRCSJ.lean.js @@ -1 +1 @@ -import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.B8l2DGq3.js";const m=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),s={name:"diskarrays.md"};function l(n,a,o,d,p,c){return e(),i("div",null,a[0]||(a[0]=[r('

DiskArrays.jl compatibility ​

DiskArrays.jl enables lazy, chunked application of:

  • broadcast

  • reductions

  • iteration

  • generators

  • zip

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.lj, GRIBDatasets.jl and CommonDataModel.jl

The combination of DiskArrays.jl and DimensionalData.jl is Julias answer to pythons xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.

They have no direct dependency relationships, with but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

',6)]))}const h=t(s,[["render",l]]);export{m as __pageData,h as default}; +import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.BAAK0EyE.js";const m=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),s={name:"diskarrays.md"};function l(n,a,o,d,p,c){return e(),i("div",null,a[0]||(a[0]=[r('

DiskArrays.jl compatibility ​

DiskArrays.jl enables lazy, chunked application of:

  • broadcast

  • reductions

  • iteration

  • generators

  • zip

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

The combination of DiskArrays.jl and DimensionalData.jl is Julia's answer to python's xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.

They have no direct dependency relationships, but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

',6)]))}const u=t(s,[["render",l]]);export{m as __pageData,u as default}; diff --git a/dev/assets/esowbav.y1SvPNdA.png b/dev/assets/esowbav.y1SvPNdA.png new file mode 100644 index 000000000..0a4932e02 Binary files /dev/null and b/dev/assets/esowbav.y1SvPNdA.png differ diff --git a/dev/assets/extending_dd.md.DoS75dmN.lean.js b/dev/assets/extending_dd.md.BXSXpgVw.js similarity index 81% rename from dev/assets/extending_dd.md.DoS75dmN.lean.js rename to dev/assets/extending_dd.md.BXSXpgVw.js index 714803264..2285c00ba 100644 --- a/dev/assets/extending_dd.md.DoS75dmN.lean.js +++ b/dev/assets/extending_dd.md.BXSXpgVw.js @@ -1,4 +1,4 @@ -import{_ as n,c as d,a4 as r,G as a,w as t,B as l,o as p,j as i,a as s}from"./chunks/framework.B8l2DGq3.js";const b=JSON.parse('{"title":"Extending DimensionalData","description":"","frontmatter":{},"headers":[],"relativePath":"extending_dd.md","filePath":"extending_dd.md","lastUpdated":null}'),o={name:"extending_dd.md"};function g(y,e,c,u,m,E){const h=l("PluginTabsTab"),k=l("PluginTabs");return p(),d("div",null,[e[2]||(e[2]=r('

Extending DimensionalData ​

Nearly everything in DimensionalData.jl is designed to be extensible.

  • AbstractDimArray are easily extended to custom array types. Raster or YAXArray are examples from other packages.

  • AbstractDimStack are easily extended to custom mixed array dataset. RasterStack or ArViZ.Dataset are examples.

  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.

dims, rebuild and format are the key interface methods in most of these cases.

dims ​

Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).

Dimension axes ​

Dimensions return from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.

This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.

dims keywords ​

To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.

This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.

rebuild ​

Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.

The function signature is always one of:

julia
rebuild(obj, args...)\nrebuild(obj; kw...)

rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.

These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.

If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.

An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.

For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.

format ​

When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:

julia
format(dims, array)

This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.

Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.

Interfaces.jl interface testing ​

DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.

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Extending DimensionalData ​

Nearly everything in DimensionalData.jl is designed to be extensible.

  • AbstractDimArray is easily extended to custom array types. Raster or YAXArray are examples from other packages.

  • AbstractDimStack is easily extended to custom mixed array datasets. RasterStack or ArViZ.Dataset are examples.

  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.

dims, rebuild and format are the key interface methods in most of these cases.

dims ​

Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).

Dimension axes ​

Dimensions returned from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.

This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.

dims keywords ​

To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.

This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.

rebuild ​

Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.

The function signature is always one of:

julia
rebuild(obj, args...)\nrebuild(obj; kw...)

rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.

These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.

If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.

An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.

For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.

format ​

When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:

julia
format(dims, array)

This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.

Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.

Interfaces.jl interface testing ​

DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.

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We can test it with:")],-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"}}," Interfaces"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"test"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(DimensionalData"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"DimArrayInterface)")])])])],-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"}),s(` `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Testing "),i("span",{style:{"--shiki-light":"#0366d6","--shiki-dark":"#2188ff"}},"DimArrayInterface"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," is implemented for "),i("span",{style:{"--shiki-light":"#0366d6","--shiki-dark":"#2188ff"}},"DimArray")]),s(` diff --git a/dev/assets/extending_dd.md.DoS75dmN.js b/dev/assets/extending_dd.md.BXSXpgVw.lean.js similarity index 81% rename from dev/assets/extending_dd.md.DoS75dmN.js rename to dev/assets/extending_dd.md.BXSXpgVw.lean.js index 714803264..2285c00ba 100644 --- a/dev/assets/extending_dd.md.DoS75dmN.js +++ b/dev/assets/extending_dd.md.BXSXpgVw.lean.js @@ -1,4 +1,4 @@ -import{_ as n,c as d,a4 as r,G as a,w as t,B as l,o as p,j as i,a as s}from"./chunks/framework.B8l2DGq3.js";const b=JSON.parse('{"title":"Extending DimensionalData","description":"","frontmatter":{},"headers":[],"relativePath":"extending_dd.md","filePath":"extending_dd.md","lastUpdated":null}'),o={name:"extending_dd.md"};function g(y,e,c,u,m,E){const h=l("PluginTabsTab"),k=l("PluginTabs");return p(),d("div",null,[e[2]||(e[2]=r('

Extending DimensionalData ​

Nearly everything in DimensionalData.jl is designed to be extensible.

  • AbstractDimArray are easily extended to custom array types. Raster or YAXArray are examples from other packages.

  • AbstractDimStack are easily extended to custom mixed array dataset. RasterStack or ArViZ.Dataset are examples.

  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.

dims, rebuild and format are the key interface methods in most of these cases.

dims ​

Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).

Dimension axes ​

Dimensions return from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.

This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.

dims keywords ​

To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.

This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.

rebuild ​

Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.

The function signature is always one of:

julia
rebuild(obj, args...)\nrebuild(obj; kw...)

rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.

These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.

If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.

An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.

For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.

format ​

When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:

julia
format(dims, array)

This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.

Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.

Interfaces.jl interface testing ​

DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.

',28)),a(k,null,{default:t(()=>[a(h,{label:"array"},{default:t(()=>e[0]||(e[0]=[i("p",null,[s("This is the implementation definition for "),i("code",null,"DimArray"),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":"#D73A49","--shiki-dark":"#F97583"}}," using"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," DimensionalData, Interfaces")]),s(` +import{_ as n,c as d,a4 as r,G as a,w as t,B as l,o as p,j as i,a as s}from"./chunks/framework.BAAK0EyE.js";const b=JSON.parse('{"title":"Extending DimensionalData","description":"","frontmatter":{},"headers":[],"relativePath":"extending_dd.md","filePath":"extending_dd.md","lastUpdated":null}'),o={name:"extending_dd.md"};function g(y,e,c,u,m,E){const h=l("PluginTabsTab"),k=l("PluginTabs");return p(),d("div",null,[e[2]||(e[2]=r('

Extending DimensionalData ​

Nearly everything in DimensionalData.jl is designed to be extensible.

  • AbstractDimArray is easily extended to custom array types. Raster or YAXArray are examples from other packages.

  • AbstractDimStack is easily extended to custom mixed array datasets. RasterStack or ArViZ.Dataset are examples.

  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.

dims, rebuild and format are the key interface methods in most of these cases.

dims ​

Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).

Dimension axes ​

Dimensions returned from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.

This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.

dims keywords ​

To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.

This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.

rebuild ​

Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.

The function signature is always one of:

julia
rebuild(obj, args...)\nrebuild(obj; kw...)

rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.

These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.

If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.

An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.

For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.

format ​

When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:

julia
format(dims, array)

This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.

Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.

Interfaces.jl interface testing ​

DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.

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Getters ​

DimensionalData.jl defines consistent methods to retrieve information from objects like DimArray, DimStack, Tuples of Dimension, Dimension and Lookup.

First we will define an example DimArray.

julia
using DimensionalData
+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.BAAK0EyE.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, Tuples 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)
╭───────────────────────────╮
@@ -7,17 +7,17 @@ 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"./ch
   ↓ 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        …  180.0       190.0       200.0
- 10      0.664038      0.464482        0.868966    0.530653    0.418968
-  9      0.00832284    0.0980829       0.314535    0.322005    0.674881
-  8      0.639212      0.233778        0.315591    0.190225    0.475569
-  7      0.602315      0.0372362       0.950143    0.062593    0.948976
-  6      0.375166      0.423798   …    0.640311    0.932968    0.98985
-  5      0.153219      0.635963        0.293126    0.68781     0.309301
-  4      0.00508449    0.829714        0.727149    0.585756    0.921145
-  3      0.946824      0.906705        0.899632    0.471877    0.633956
-  2      0.876112      0.831617        0.128932    0.428951    0.243803
-  1      0.749253      0.864128   …    0.715102    0.921012    0.566574
`,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 you can filter which dimensions you want 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(`
+  ↓ →  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"}}," 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dev/assets/get_info.md.oVG65ArA.lean.js index 0cb00aba1..1c41ba988 100644 --- a/dev/assets/get_info.md.B7K6LY7P.lean.js +++ b/dev/assets/get_info.md.oVG65ArA.lean.js @@ -1,4 +1,4 @@ -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.B8l2DGq3.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, Tuples of Dimension, Dimension and Lookup.

First we will define an example DimArray.

julia
using DimensionalData
+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.BAAK0EyE.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, Tuples 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)
╭───────────────────────────╮
@@ -7,17 +7,17 @@ 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"./ch
   ↓ 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        …  180.0       190.0       200.0
- 10      0.664038      0.464482        0.868966    0.530653    0.418968
-  9      0.00832284    0.0980829       0.314535    0.322005    0.674881
-  8      0.639212      0.233778        0.315591    0.190225    0.475569
-  7      0.602315      0.0372362       0.950143    0.062593    0.948976
-  6      0.375166      0.423798   …    0.640311    0.932968    0.98985
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Predicates ​

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Predicates ​

These always return true or false. With multiple dimensions, false means !all and true means all.

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9f6cbbb9b..aaf3e0a1d 100644 --- a/dev/assets/groupby.md.DESSK1W0.js +++ b/dev/assets/groupby.md.BUxX6owS.js @@ -1,7 +1,7 @@ -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.B8l2DGq3.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 will cover:

  • simple grouping with a function

  • grouping with Bins

  • grouping with another existing AbstractDimArry or Dimension

Grouping functions ​

Lets 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
+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.BAAK0EyE.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:
  • simple grouping with a function
  • grouping with Bins
  • grouping with another existing AbstractDimArray or Dimension
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")
Lets see how some common functions work.
The hour function will transform values to 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(`
+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 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`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")]),s(` `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")]),s(` -`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t(`

Grouping and reducing ​

Lets define an array with a time dimension of the times used above:

julia
julia> A = rand(X(1:0.01:2), Ti(tempo))
╭───────────────────────────────╮
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t(`
Grouping and reducing ​
Let's define an array with a time dimension of the times used above:
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   0.247032              0.706417                 0.10748
- 1.01  0.749267              0.559878                 0.916216
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F=p(g,[["render",y]]);export{b as __pageData,F as default}; diff --git a/dev/assets/groupby.md.DESSK1W0.lean.js b/dev/assets/groupby.md.BUxX6owS.lean.js similarity index 89% rename from dev/assets/groupby.md.DESSK1W0.lean.js rename to dev/assets/groupby.md.BUxX6owS.lean.js index 9f6cbbb9b..aaf3e0a1d 100644 --- a/dev/assets/groupby.md.DESSK1W0.lean.js +++ b/dev/assets/groupby.md.BUxX6owS.lean.js @@ -1,7 +1,7 @@ -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.B8l2DGq3.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 will cover:

  • simple grouping with a function

  • grouping with Bins

  • grouping with another existing AbstractDimArry or Dimension

Grouping functions ​

Lets 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
+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.BAAK0EyE.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:
  • simple grouping with a function
  • grouping with Bins
  • grouping with another existing AbstractDimArray or Dimension
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")
Lets see how some common functions work.
The hour function will transform values to 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(`
+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 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`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")]),s(` `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")]),s(` -`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t(`

Grouping and reducing ​

Lets define an array with a time dimension of the times used above:

julia
julia> A = rand(X(1:0.01:2), Ti(tempo))
╭───────────────────────────────╮
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (2001, 364)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t(`
Grouping and reducing ​
Let's define an array with a time dimension of the times used above:
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   0.247032              0.706417                 0.10748
- 1.01  0.749267              0.559878                 0.916216
- 1.02  0.694796              0.981865                 0.842792
- 1.03  0.214909              0.313279                 0.683712
+ 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  0.899864              0.819928                 0.313711
- 1.97  0.581748              0.0446036                0.762533
- 1.98  0.645795              0.442542                 0.522989
- 1.99  0.460633              0.346013              …  0.639499
- 2.0   0.0403793             0.561611                 0.736525
`,4)),e(k,null,{default:l(()=>[e(h,{label:"basic"},{default:l(()=>a[8]||(a[8]=[i("p",null,[s("Group by month, using the "),i("code",null,"month"),s(" function:")],-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"}}," groups "),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," groupby"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(A, Ti"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"=>"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"month)")])])])],-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(`
+ 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
`,4)),e(k,null,{default:l(()=>[e(h,{label:"basic"},{default:l(()=>a[8]||(a[8]=[i("p",null,[s("Group by month, using the "),i("code",null,"month"),s(" function:")],-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"}}," groups "),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," groupby"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(A, 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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).

Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.

A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSampled lookups.

YAXArrays.jl ​

YAXArrays.jl is another spatial data package aimed more at (very) large datasets. Its functionality is slowly converging with Rasters.jl (both wrapping DiskArrays.jl/DimensionalData.jl) and we work closely with the developers.

YAXArray is a AbstractDimArray and inherits its behaviours.

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.

An ArviZ.Dataset is an AbstractDimStack!

JuMP.jl ​

JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.

CryoGrid.jl ​

CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.

CryoGridOutput uses DimArray for views into output data.

DynamicGrids.jl ​

DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.

All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!

AstroImages.jl ​

AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.

TimeseriesTools.jl ​

TimeseriesTools.jl Uses DimArray for time-series data.

',25)]))}const u=e(s,[["render",i]]);export{p as __pageData,u as default}; diff --git a/dev/assets/integrations.md.CffVJNNA.lean.js b/dev/assets/integrations.md.CffVJNNA.lean.js new file mode 100644 index 000000000..3314d4eb7 --- /dev/null +++ b/dev/assets/integrations.md.CffVJNNA.lean.js @@ -0,0 +1 @@ +import{_ as e,c as r,a4 as t,o}from"./chunks/framework.BAAK0EyE.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).

Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.

A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSampled lookups.

YAXArrays.jl ​

YAXArrays.jl is another spatial data package aimed more at (very) large datasets. Its functionality is slowly converging with Rasters.jl (both wrapping DiskArrays.jl/DimensionalData.jl) and we work closely with the developers.

YAXArray is a AbstractDimArray and inherits its behaviours.

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.

An ArviZ.Dataset is an AbstractDimStack!

JuMP.jl ​

JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.

CryoGrid.jl ​

CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.

CryoGridOutput uses DimArray for views into output data.

DynamicGrids.jl ​

DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.

All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!

AstroImages.jl ​

AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.

TimeseriesTools.jl ​

TimeseriesTools.jl Uses DimArray for time-series data.

',25)]))}const u=e(s,[["render",i]]);export{p as __pageData,u as default}; diff --git a/dev/assets/integrations.md.dJo1qUjg.js b/dev/assets/integrations.md.dJo1qUjg.js deleted file mode 100644 index 810aa2cb8..000000000 --- a/dev/assets/integrations.md.dJo1qUjg.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,c as r,a4 as t,o}from"./chunks/framework.B8l2DGq3.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 DD for geospatial data manipulation, providing file load/save for a wide range of raster data sources and common GIS tools like polygon rasterization and masking. Raster types are aware of crs and their missingval (which is often not missing for performance and storage reasons).

Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.

A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSample lookups.

YAXArrays.jl ​

YAXArrays.jl is another spatial data package aimed more at (very) large datasets. It's functionality is slowly converging with Rasters.jl (both wrapping DiskArray.jl/DimensionalData.jl) and we work closely with the developers.

YAXArray is a AbstractDimArray and inherits its behaviours.

ClimateBase.jl ​

ClimateBase.jl Extends DD with methods for analysis of climate data.

ArviZ.jl ​

ArviZ.jl Is a Julia package for exploratory analysis of Bayesian models.

An ArviZ.Dataset is an AbstractDimStack!

JuMP.jl ​

JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.

CryoGrid.jl ​

CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.

CryoGridOutput uses DimArray for views into output data.

DynamicGrids.jl ​

DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.

All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!

AstroImages.jl ​

AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.

TimeseriesTools.jl ​

TimeseriesTools.jl Uses DimArray for time-series data.

',25)]))}const u=e(s,[["render",i]]);export{p as __pageData,u as default}; diff --git a/dev/assets/integrations.md.dJo1qUjg.lean.js b/dev/assets/integrations.md.dJo1qUjg.lean.js deleted file mode 100644 index 810aa2cb8..000000000 --- a/dev/assets/integrations.md.dJo1qUjg.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,c as r,a4 as t,o}from"./chunks/framework.B8l2DGq3.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 DD for geospatial data manipulation, providing file load/save for a wide range of raster data sources and common GIS tools like polygon rasterization and masking. Raster types are aware of crs and their missingval (which is often not missing for performance and storage reasons).

Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.

A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSample lookups.

YAXArrays.jl ​

YAXArrays.jl is another spatial data package aimed more at (very) large datasets. It's functionality is slowly converging with Rasters.jl (both wrapping DiskArray.jl/DimensionalData.jl) and we work closely with the developers.

YAXArray is a AbstractDimArray and inherits its behaviours.

ClimateBase.jl ​

ClimateBase.jl Extends DD with methods for analysis of climate data.

ArviZ.jl ​

ArviZ.jl Is a Julia package for exploratory analysis of Bayesian models.

An ArviZ.Dataset is an AbstractDimStack!

JuMP.jl ​

JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.

CryoGrid.jl ​

CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.

CryoGridOutput uses DimArray for views into output data.

DynamicGrids.jl ​

DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.

All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!

AstroImages.jl ​

AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.

TimeseriesTools.jl ​

TimeseriesTools.jl Uses DimArray for time-series data.

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Modifying objects ​

DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.

Everything else must be rebuilt and assigned to a variable.

modify ​

Modify the inner arrays of a AbstractDimArray or AbstractDimStack, with modify. This can be useful to e.g. replace all arrays with CuArray moving the data to the GPU, collect all inner arrays to Array without losing the outer DimArray wrappers, and similar things.

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Modifying Objects ​

DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.

Everything else must be rebuilt and assigned to a variable.

modify ​

Modify the inner arrays of a AbstractDimArray or AbstractDimStack, with modify. This can be useful to e.g. replace all arrays with CuArray moving the data to the GPU, collect all inner arrays to Array without losing the outer DimArray wrappers, and similar things.

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mergedims ​

mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.

rebuild ​

rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.

Warning

rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.

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mergedims ​

mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.

rebuild ​

rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.

Warning

rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.

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"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> d = X(1)
X 1
julia
julia> rebuild(d, 1:10)
X 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.

TypeKeywordsArguments
AbstractDimArraydata, dims, [refdims, name, metadata]as with kw, in order
AbstractDimStackdata, dims, [refdims], layerdims, [metadata, layermetadata]as with kw, in order
Dimensionvalval
Selectorval, (atol)val
Lookupdata, (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)
(a = 1, b = 2)
julia
julia> rebuild(nt, a = 99)
(a = 99, b = 2)

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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Modifying objects ​

DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.

Everything else must be rebuilt and assigned to a variable.

modify ​

Modify the inner arrays of a AbstractDimArray or AbstractDimStack, with modify. This can be useful to e.g. replace all arrays with CuArray moving the data to the GPU, collect all inner arrays to Array without losing the outer DimArray wrappers, and similar things.

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Modifying Objects ​

DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.

Everything else must be rebuilt and assigned to a variable.

modify ​

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mergedims ​

mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.

rebuild ​

rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.

Warning

rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.

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mergedims ​

mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.

rebuild ​

rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.

Warning

rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.

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"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> d = X(1)
X 1
julia
julia> rebuild(d, 1:10)
X 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.

TypeKeywordsArguments
AbstractDimArraydata, dims, [refdims, name, metadata]as with kw, in order
AbstractDimStackdata, dims, [refdims], layerdims, [metadata, layermetadata]as with kw, in order
Dimensionvalval
Selectorval, (atol)val
Lookupdata, (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)
(a = 1, b = 2)
julia
julia> rebuild(nt, a = 99)
(a = 99, b = 2)

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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"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"'a':1:'n'"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Unordered")]),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'"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," … "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"'l'"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," 'm'"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," 'n'")]),s(` -`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.450109 0.0702987 0.28035 0.183103 0.770553 0.352592")]),s(` -`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.668984 0.803446 0.406397 0.138819 0.21316 0.907202")]),s(` -`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 3.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.338844 0.461631 0.207882 0.804846 0.69461 0.340557")])])])],-1),i("p",null,":::",-1)])),_:1})]),_:1})])}const v=d(g,[["render",y]]);export{m as __pageData,v as default}; +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.159092 0.193627 0.317813 0.474755 0.474052 0.364844")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.903705 0.0915867 0.247032 0.824556 0.298728 0.933212")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 3.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 0.884218 0.686915 0.208271 0.744517 0.487197 0.86789")])])])],-1),i("p",null,":::",-1)])),_:1})]),_:1})])}const v=d(g,[["render",y]]);export{m as __pageData,v as default}; diff --git a/dev/assets/plots.md.kpY9w3i0.js b/dev/assets/plots.md.D4kZ1izt.js similarity index 96% rename from dev/assets/plots.md.kpY9w3i0.js rename to dev/assets/plots.md.D4kZ1izt.js index b8e46c42a..4554d0b32 100644 --- a/dev/assets/plots.md.kpY9w3i0.js +++ b/dev/assets/plots.md.D4kZ1izt.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.B8l2DGq3.js";const e="/DimensionalData.jl/dev/assets/jmmhsxu.iiL4UBgm.png",h="/DimensionalData.jl/dev/assets/ybmzrjc.BIa4VhRY.png",n="/DimensionalData.jl/dev/assets/veptoqy.CZKDtjAN.png",p="/DimensionalData.jl/dev/assets/izkfogt.nOjqWh1g.png",k="/DimensionalData.jl/dev/assets/yhxwiti.CBNkYhiq.png",r="/DimensionalData.jl/dev/assets/rcywifo.NnnTWDGw.png",m=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),d={name:"plots.md"};function o(E,s,g,c,y,F){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
+import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.BAAK0EyE.js";const e="/DimensionalData.jl/dev/assets/esowbav.y1SvPNdA.png",h="/DimensionalData.jl/dev/assets/iqbzbbw.CmaGXDA6.png",n="/DimensionalData.jl/dev/assets/jrsvknx.DWa7L8iX.png",p="/DimensionalData.jl/dev/assets/garsckr.CNIu1Nb4.png",k="/DimensionalData.jl/dev/assets/kddpcoo.DDw-uyO9.png",r="/DimensionalData.jl/dev/assets/dcljjmm.DHFgsALw.png",m=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),d={name:"plots.md"};function o(E,s,g,c,y,F){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:
julia
Makie.rainclouds(A)
Test series plots ​
default colormap ​
julia
B = rand(X(10:10:100), Y([:a, :b, :c, :d, :e, :f, :g, :h, :i, :j]))
diff --git a/dev/assets/plots.md.kpY9w3i0.lean.js b/dev/assets/plots.md.D4kZ1izt.lean.js
similarity index 96%
rename from dev/assets/plots.md.kpY9w3i0.lean.js
rename to dev/assets/plots.md.D4kZ1izt.lean.js
index b8e46c42a..4554d0b32 100644
--- a/dev/assets/plots.md.kpY9w3i0.lean.js
+++ b/dev/assets/plots.md.D4kZ1izt.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.B8l2DGq3.js";const e="/DimensionalData.jl/dev/assets/jmmhsxu.iiL4UBgm.png",h="/DimensionalData.jl/dev/assets/ybmzrjc.BIa4VhRY.png",n="/DimensionalData.jl/dev/assets/veptoqy.CZKDtjAN.png",p="/DimensionalData.jl/dev/assets/izkfogt.nOjqWh1g.png",k="/DimensionalData.jl/dev/assets/yhxwiti.CBNkYhiq.png",r="/DimensionalData.jl/dev/assets/rcywifo.NnnTWDGw.png",m=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),d={name:"plots.md"};function o(E,s,g,c,y,F){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
+import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.BAAK0EyE.js";const e="/DimensionalData.jl/dev/assets/esowbav.y1SvPNdA.png",h="/DimensionalData.jl/dev/assets/iqbzbbw.CmaGXDA6.png",n="/DimensionalData.jl/dev/assets/jrsvknx.DWa7L8iX.png",p="/DimensionalData.jl/dev/assets/garsckr.CNIu1Nb4.png",k="/DimensionalData.jl/dev/assets/kddpcoo.DDw-uyO9.png",r="/DimensionalData.jl/dev/assets/dcljjmm.DHFgsALw.png",m=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),d={name:"plots.md"};function o(E,s,g,c,y,F){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:
julia
Makie.rainclouds(A)
Test series plots ​
default colormap ​
julia
B = rand(X(10:10:100), Y([:a, :b, :c, :d, :e, :f, :g, :h, :i, :j]))
diff --git a/dev/assets/rcywifo.NnnTWDGw.png b/dev/assets/rcywifo.NnnTWDGw.png
deleted file mode 100644
index 3653bcd6f..000000000
Binary files a/dev/assets/rcywifo.NnnTWDGw.png and /dev/null differ
diff --git a/dev/assets/selectors.md.Cu8DY1IK.js b/dev/assets/selectors.md.BYwWS6hp.js
similarity index 63%
rename from dev/assets/selectors.md.Cu8DY1IK.js
rename to dev/assets/selectors.md.BYwWS6hp.js
index 334341268..f7e97ce8d 100644
--- a/dev/assets/selectors.md.Cu8DY1IK.js
+++ b/dev/assets/selectors.md.BYwWS6hp.js
@@ -1,48 +1,48 @@
-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.B8l2DGq3.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 ​
As well as choosing dimensions by name, we can also select values in them.
First, we can create 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]))
╭─────────────────────────╮
+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.BAAK0EyE.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  0.772277  0.536911   0.101231
- 1.2  0.711133  0.0653496  0.339858
- 1.4  0.883222  0.748041   0.191494
- 1.6  0.802776  0.621603   0.341976
- 1.8  0.156538  0.768488   0.87255
- 2.0  0.969079  0.869012   0.415714
Then we can use Selector to select values from the array:
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+ ↓ →   :a        :b        :c
+ 1.0  0.782081  0.381692  0.751977
+ 1.2  0.836407  0.992013  0.806006
+ 1.4  0.429267  0.367576  0.376108
+ 1.6  0.127082  0.114532  0.0943594
+ 1.8  0.677743  0.354013  0.910098
+ 2.0  0.822783  0.874735  0.144269
Then we can use the Selector to select values from the array:
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.822783  0.144269")])])])],-1)])),_:1}),l(e,{label:"Not"},{default:h(()=>a[6]||(a[6]=[i("p",null,[s("The "),i("code",null,"Not(x)"),s(" selector gets all indices "),i("em",null,"not"),s(" selected by "),i("code",null,"x"),s(", which can be another selector. "),i("code",null,"Not"),s(" is like indexing with a "),i("code",null,"Vector{Bool}"),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"}},"Not"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Near"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.3"),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"}},"Not"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Where"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"in"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":a"),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":"#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"}},"5"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1"),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, 1.6, 1.8, 2.0]"),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"}},"[:b]"),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"}},"   :b")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.536911")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.0653496")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.621603")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.8"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.768488")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.869012")])])])],-1)])),_:1})]),_:1}),a[12]||(a[12]=k('
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
',3)),l(t,null,{default:h(()=>[l(e,{label:"Sampled lookups"},{default:h(()=>a[7]||(a[7]=[i("p",null,[i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Sampled"},[i("code",null,"Sampled(x)")]),s(" lookups hold values sampled along an axis. They may be "),i("code",null,"Ordered"),s("/"),i("code",null,"Unordered"),s(", "),i("code",null,"Intervals"),s("/"),i("code",null,"Points"),s(", and "),i("code",null,"Regular"),s("/"),i("code",null,"Irregular"),s(".")],-1),i("p",null,[s("Most of these properties are usually detected automatically, but here we create a "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Sampled"},[i("code",null,"Sampled")]),s(" lookup manually:")],-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"}}," Sampled"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"100.0"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"; order"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(), span"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Regular"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), sampling"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Intervals"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Start"),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"}},"Sampled{Float64} "),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Regular"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Intervals{Start}")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"wrapping: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"10.0:10.0:100.0")])])])],-1),i("p",null,[s("To specify "),i("code",null,"Irregular"),s(),i("code",null,"Intervals"),s(" we should include the outer bounds of the lookup, as we cant determine them from the vector.")],-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"}}," Sampled"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(["),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"13"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"8"),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"}},"3"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"2"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"]; order"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(), span"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Irregular"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"21"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), sampling"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Intervals"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Start"),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"}},"Sampled{Int64} "),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"}}," Intervals{Start}")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.381692")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.992013")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.114532")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.8"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.354013")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.874735")])])])],-1)])),_:1})]),_:1}),a[12]||(a[12]=k('
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
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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])))
╭─────────────────────────╮
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-12-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},'  "Dec"')])])])],-1),i("p",null,"Now we can select any date and get the month:",-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["),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"}},"DateTime"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"2005"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"4"),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"}},'"Apr"')])])])],-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["),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"}},"DateTime"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"3047"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"9"),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"}},'"Sep"')])])])],-1)])),_:1}),l(e,{label:"NoLookup"},{default:h(()=>a[10]||(a[10]=[i("p",null,[s("The "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.NoLookup"},[i("code",null,"NoLookup(x)")]),s(" lookup has no lookup values provided, so "),i("code",null,"Selector"),s("s will not work. When you create a "),i("code",null,"DimArray"),s(" without a lookup array, "),i("code",null,"NoLookup"),s(" will be used. It is also not shown in REPL printing.")],-1),i("p",null,[s("Here we create a "),i("a",{href:"/DimensionalData.jl/dev/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    0.441181  0.12452   0.201129  0.12668   0.647225
- 20    0.621662  0.196478  0.792553  0.575595  0.357367
- 30    0.72217   0.791844  0.883323  0.915251  0.246886
- 40    0.896257  0.758149  0.679453  0.506221  0.667841
- 50    0.301659  0.229418  0.442111  0.680987  0.0429074
- 60    0.973622  0.228248  0.882656  0.396585  0.870348
- 70    0.942925  0.749731  0.683795  0.687921  0.825204
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 Selectors At, Near or Contains.
julia
julia> A = rand(X(1.0:0.2:2.0), Y(10:2:20))
╭─────────────────────────╮
+ 10    0.659048  0.883222  0.621603  0.41767   0.48849
+ 20    0.796999  0.872742  0.768488  0.594101  0.956886
+ 30    0.27736   0.530405  0.721125  0.530389  0.00114293
+ 40    0.444305  0.226064  0.806166  0.794725  0.522816
+ 50    0.184738  0.764895  0.496058  0.298029  0.305342
+ 60    0.772277  0.86273   0.973357  0.491103  0.927836
+ 70    0.711133  0.748041  0.925367  0.976465  0.239921
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   0.11787     0.664642   0.0831822   0.528009   0.631878   0.990294
- 1.2   0.0905873   0.30391    0.552153    0.229792   0.29205    0.610173
- 1.4   0.495624    0.810377   0.578253    0.574678   0.371583   0.460775
- 1.6   0.263531    0.361536   0.753597    0.568763   0.564657   0.788252
- 1.8   0.388768    0.44818    0.0300922   0.896624   0.952489   0.216905
- 2.0   0.422318    0.499307   0.228081    0.823123   0.10454    0.931705
We can define another array with partly matching indices
julia
julia> B = rand(X(1.0:0.04:2.0), Y(20:-1:10))
╭───────────────────────────╮
+ ↓ →  10          12           14         16         18         20
+ 1.0   0.672504    0.72217      0.196478   0.650675   0.238831   0.396585
+ 1.2   0.71372     0.896257     0.791844   0.97293    0.570603   0.687921
+ 1.4   0.905616    0.00498986   0.758149   0.883323   0.929034   0.880178
+ 1.6   0.0928922   0.307762     0.229418   0.679453   0.21921    0.855976
+ 1.8   0.441181    0.651538     0.75985    0.442111   0.506221   0.0575993
+ 2.0   0.621662    0.314906     0.152068   0.882656   0.680987   0.771237
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         18         …  12          11         10
- 1.0    0.300304    0.660657   0.677543      0.712342    0.806495   0.727464
- 1.04   0.421525    0.835674   0.458427      0.248873    0.412078   0.455255
- 1.08   0.399781    0.824659   0.392334      0.675459    0.95213    0.91666
- 1.12   0.717006    0.623124   0.482654      0.964964    0.304266   0.313361
+ 1.0    0.0775482   0.473944   0.898833      0.746037    0.594523   0.312277
+ 1.04   0.838572    0.631878   0.429465      0.812342    0.737151   0.5053
+ 1.08   0.166221    0.29205    0.244582      0.363678    0.801242   0.328169
+ 1.12   0.11787     0.371583   0.400001      0.92906     0.337296   0.760043
  ⋮                                       ⋱               ⋮
- 1.84   0.880853    0.578329   0.394576      0.255579    0.750947   0.27206
- 1.88   0.363918    0.533041   0.545522      0.516268    0.531012   0.503183
- 1.92   0.259597    0.358535   0.748884      0.575727    0.227865   0.865713
- 1.96   0.34589     0.94196    0.657054  …   0.728852    0.462859   0.767151
- 2.0    0.0143204   0.985407   0.476387      0.0417586   0.286      0.777391
And we can simply select values from B with selectors from A:
julia
julia> B[DimSelectors(A)]
╭─────────────────────────╮
+ 1.84   0.528009    0.951796   0.188707      0.0411113   0.608263   0.993001
+ 1.88   0.229792    0.565165   0.910836      0.162784    0.410125   0.699118
+ 1.92   0.574678    0.618541   0.187648      0.952744    0.583476   0.460775
+ 1.96   0.89559     0.812106   0.953145  …   0.727405    0.559465   0.788252
+ 2.0    0.595409    0.630782   0.298791      0.391258    0.320737   0.216905
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   0.727464   0.712342    0.14203    0.207735    0.677543   0.300304
- 1.2   0.815253   0.642187    0.49973    0.0387029   0.319496   0.887979
- 1.4   0.391307   0.197423    0.166425   0.48124     0.300285   0.0574938
- 1.6   0.49224    0.795274    0.24625    0.735391    0.699976   0.973586
- 1.8   0.539197   0.0661958   0.811917   0.822008    0.848624   0.930699
- 2.0   0.777391   0.0417586   0.873218   0.834418    0.476387   0.0143204
If the lookups aren't aligned we can use Near instead of At, which like doing a nearest neighbor interpolation:
julia
julia> C = rand(X(1.0:0.007:2.0), Y(10.0:0.9:30))
╭────────────────────────────╮
+ ↓ →  10         12         14          16          18          20
+ 1.0   0.312277   0.746037   0.0623229   0.930614    0.898833    0.0775482
+ 1.2   0.825676   0.208304   0.232648    0.856029    0.649595    0.495624
+ 1.4   0.87475    0.403556   0.0990865   0.571126    0.0564029   0.909826
+ 1.6   0.939546   0.097066   0.139173    0.0566306   0.833475    0.0831822
+ 1.8   0.747695   0.721253   0.998253    0.781923    0.554833    0.228081
+ 2.0   0.216905   0.391258   0.719849    0.127621    0.298791    0.595409
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     0.168617     0.417756       0.258764    0.195897    0.747615
- 1.007   0.395053     0.994295       0.770101    0.403836    0.891596
- 1.014   0.144422     0.370454       0.878565    0.753744    0.0149561
- 1.021   0.938771     0.917143       0.679296    0.180397    0.436168
- ⋮                               ⋱
- 1.966   0.454047     0.92977        0.662833    0.160403    0.276742
- 1.973   0.846736     0.0923954  …   0.43915     0.405912    0.245638
- 1.98    0.946655     0.0506805      0.0821229   0.0787968   0.613895
- 1.987   0.00686195   0.266013       0.485952    0.193179    0.0628947
- 1.994   0.84475      0.574619       0.0760101   0.950007    0.662413
julia
julia> C[DimSelectors(A; selectors=Near)]
╭─────────────────────────╮
+ ↓ →    10.0       10.9       11.8        …  28.0       28.9       29.8
+ 1.0     0.412631   0.707629   0.640577       0.329029   0.945549   0.505129
+ 1.007   0.300304   0.66424    0.948532       0.565649   0.27236    0.782344
+ 1.014   0.421525   0.564636   0.267693       0.861783   0.53422    0.906871
+ 1.021   0.399781   0.381315   0.970303       0.449093   0.560553   0.565202
+ ⋮                                        ⋱
+ 1.966   0.608086   0.605375   0.336929       0.891637   0.537974   0.509288
+ 1.973   0.500679   0.179953   0.183503   …   0.851044   0.206886   0.824656
+ 1.98    0.673494   0.613381   0.0136364      0.427523   0.397621   0.742451
+ 1.987   0.294489   0.949143   0.520037       0.307031   0.18378    0.531598
+ 1.994   0.725774   0.525431   0.870641       0.919448   0.09814    0.331422
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     0.168617    0.397386    0.133475    0.0630398   0.247976   0.145293
- 1.203   0.233685    0.243195    0.331147    0.584048    0.717841   0.832538
- 1.399   0.0164892   0.729706    0.0315018   0.899356    0.847872   0.358513
- 1.602   0.0426808   0.61781     0.808889    0.140817    0.188976   0.054083
- 1.798   0.226699    0.0983553   0.661444    0.750622    0.650842   0.986606
- 1.994   0.84475     0.366873    0.734751    0.590953    0.621527   0.23502
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
+ ↓ →    10.0        11.8       13.6       16.3        18.1       19.9
+ 1.0     0.412631    0.640577   0.505726   0.371838    0.49487    0.0639461
+ 1.203   0.824659    0.913321   0.510752   0.29488     0.154653   0.647062
+ 1.399   0.366634    0.653364   0.568727   0.925888    0.660823   0.645043
+ 1.602   0.0515532   0.324073   0.424831   0.808144    0.838211   0.71953
+ 1.798   0.205861    0.414017   0.453507   0.526825    0.180915   0.125626
+ 1.994   0.725774    0.870641   0.484227   0.0294193   0.6675     0.712805
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
diff --git a/dev/assets/selectors.md.Cu8DY1IK.lean.js b/dev/assets/selectors.md.BYwWS6hp.lean.js
similarity index 63%
rename from dev/assets/selectors.md.Cu8DY1IK.lean.js
rename to dev/assets/selectors.md.BYwWS6hp.lean.js
index 334341268..f7e97ce8d 100644
--- a/dev/assets/selectors.md.Cu8DY1IK.lean.js
+++ b/dev/assets/selectors.md.BYwWS6hp.lean.js
@@ -1,48 +1,48 @@
-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.B8l2DGq3.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 ​
As well as choosing dimensions by name, we can also select values in them.
First, we can create 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]))
╭─────────────────────────╮
+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.BAAK0EyE.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  0.772277  0.536911   0.101231
- 1.2  0.711133  0.0653496  0.339858
- 1.4  0.883222  0.748041   0.191494
- 1.6  0.802776  0.621603   0.341976
- 1.8  0.156538  0.768488   0.87255
- 2.0  0.969079  0.869012   0.415714
Then we can use 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,[i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.At"},[i("code",null,"At(x)")]),s(" 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"}},"0.33985840915074383")])])])],-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(`
+ ↓ →   :a        :b        :c
+ 1.0  0.782081  0.381692  0.751977
+ 1.2  0.836407  0.992013  0.806006
+ 1.4  0.429267  0.367576  0.376108
+ 1.6  0.127082  0.114532  0.0943594
+ 1.8  0.677743  0.354013  0.910098
+ 2.0  0.822783  0.874735  0.144269
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/dev/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"}},"0.806005979085237")])])])],-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(`
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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":"#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(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.772277  0.536911   0.101231")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.711133  0.0653496  0.339858")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.4"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.883222  0.748041   0.191494")])])])],-1),i("p",null,[i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.At"},[i("code",null,"At")]),s(" can also take vectors and ranges:")],-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":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.2"),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"}},"), 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"}},":a"),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":"#959da5","--shiki-dark":"#959da5"}},"╭─────────────────────────╮")]),s(`
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.822783  0.144269")])])])],-1)])),_:1}),l(e,{label:"Not"},{default:h(()=>a[6]||(a[6]=[i("p",null,[s("The "),i("code",null,"Not(x)"),s(" selector gets all indices "),i("em",null,"not"),s(" selected by "),i("code",null,"x"),s(", which can be another selector. "),i("code",null,"Not"),s(" is like indexing with a "),i("code",null,"Vector{Bool}"),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"}},"Not"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Near"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.3"),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"}},"Not"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Where"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"in"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":a"),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":"#959da5","--shiki-dark":"#959da5"}},"╭─────────────────────────╮")]),s(`
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 `),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, 1.6, 1.8, 2.0]"),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"}},"[:b]"),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"}},"   :b")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.536911")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.0653496")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.621603")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.8"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.768488")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.869012")])])])],-1)])),_:1})]),_:1}),a[12]||(a[12]=k('
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
',3)),l(t,null,{default:h(()=>[l(e,{label:"Sampled lookups"},{default:h(()=>a[7]||(a[7]=[i("p",null,[i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Sampled"},[i("code",null,"Sampled(x)")]),s(" lookups hold values sampled along an axis. They may be "),i("code",null,"Ordered"),s("/"),i("code",null,"Unordered"),s(", "),i("code",null,"Intervals"),s("/"),i("code",null,"Points"),s(", and "),i("code",null,"Regular"),s("/"),i("code",null,"Irregular"),s(".")],-1),i("p",null,[s("Most of these properties are usually detected automatically, but here we create a "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.Sampled"},[i("code",null,"Sampled")]),s(" lookup manually:")],-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"}}," Sampled"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"100.0"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"; order"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(), span"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Regular"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10.0"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), sampling"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Intervals"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Start"),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"}},"Sampled{Float64} "),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Regular"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Intervals{Start}")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"wrapping: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"10.0:10.0:100.0")])])])],-1),i("p",null,[s("To specify "),i("code",null,"Irregular"),s(),i("code",null,"Intervals"),s(" we should include the outer bounds of the lookup, as we cant determine them from the vector.")],-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"}}," Sampled"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(["),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"13"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"8"),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"}},"3"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"2"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"]; order"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"ForwardOrdered"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(), span"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Irregular"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"21"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), sampling"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Intervals"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Start"),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"}},"Sampled{Int64} "),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"}}," Intervals{Start}")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.381692")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.992013")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.114532")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 1.8"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.354013")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.874735")])])])],-1)])),_:1})]),_:1}),a[12]||(a[12]=k('
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
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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])))
╭─────────────────────────╮
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-12-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},'  "Dec"')])])])],-1),i("p",null,"Now we can select any date and get the month:",-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["),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"}},"DateTime"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"2005"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"4"),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"}},'"Apr"')])])])],-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["),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"}},"DateTime"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"3047"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"9"),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"}},'"Sep"')])])])],-1)])),_:1}),l(e,{label:"NoLookup"},{default:h(()=>a[10]||(a[10]=[i("p",null,[s("The "),i("a",{href:"/DimensionalData.jl/dev/api/lookuparrays#DimensionalData.Dimensions.Lookups.NoLookup"},[i("code",null,"NoLookup(x)")]),s(" lookup has no lookup values provided, so "),i("code",null,"Selector"),s("s will not work. When you create a "),i("code",null,"DimArray"),s(" without a lookup array, "),i("code",null,"NoLookup"),s(" will be used. It is also not shown in REPL printing.")],-1),i("p",null,[s("Here we create a "),i("a",{href:"/DimensionalData.jl/dev/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    0.441181  0.12452   0.201129  0.12668   0.647225
- 20    0.621662  0.196478  0.792553  0.575595  0.357367
- 30    0.72217   0.791844  0.883323  0.915251  0.246886
- 40    0.896257  0.758149  0.679453  0.506221  0.667841
- 50    0.301659  0.229418  0.442111  0.680987  0.0429074
- 60    0.973622  0.228248  0.882656  0.396585  0.870348
- 70    0.942925  0.749731  0.683795  0.687921  0.825204
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 Selectors At, Near or Contains.
julia
julia> A = rand(X(1.0:0.2:2.0), Y(10:2:20))
╭─────────────────────────╮
+ 10    0.659048  0.883222  0.621603  0.41767   0.48849
+ 20    0.796999  0.872742  0.768488  0.594101  0.956886
+ 30    0.27736   0.530405  0.721125  0.530389  0.00114293
+ 40    0.444305  0.226064  0.806166  0.794725  0.522816
+ 50    0.184738  0.764895  0.496058  0.298029  0.305342
+ 60    0.772277  0.86273   0.973357  0.491103  0.927836
+ 70    0.711133  0.748041  0.925367  0.976465  0.239921
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   0.11787     0.664642   0.0831822   0.528009   0.631878   0.990294
- 1.2   0.0905873   0.30391    0.552153    0.229792   0.29205    0.610173
- 1.4   0.495624    0.810377   0.578253    0.574678   0.371583   0.460775
- 1.6   0.263531    0.361536   0.753597    0.568763   0.564657   0.788252
- 1.8   0.388768    0.44818    0.0300922   0.896624   0.952489   0.216905
- 2.0   0.422318    0.499307   0.228081    0.823123   0.10454    0.931705
We can define another array with partly matching indices
julia
julia> B = rand(X(1.0:0.04:2.0), Y(20:-1:10))
╭───────────────────────────╮
+ ↓ →  10          12           14         16         18         20
+ 1.0   0.672504    0.72217      0.196478   0.650675   0.238831   0.396585
+ 1.2   0.71372     0.896257     0.791844   0.97293    0.570603   0.687921
+ 1.4   0.905616    0.00498986   0.758149   0.883323   0.929034   0.880178
+ 1.6   0.0928922   0.307762     0.229418   0.679453   0.21921    0.855976
+ 1.8   0.441181    0.651538     0.75985    0.442111   0.506221   0.0575993
+ 2.0   0.621662    0.314906     0.152068   0.882656   0.680987   0.771237
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         18         …  12          11         10
- 1.0    0.300304    0.660657   0.677543      0.712342    0.806495   0.727464
- 1.04   0.421525    0.835674   0.458427      0.248873    0.412078   0.455255
- 1.08   0.399781    0.824659   0.392334      0.675459    0.95213    0.91666
- 1.12   0.717006    0.623124   0.482654      0.964964    0.304266   0.313361
+ 1.0    0.0775482   0.473944   0.898833      0.746037    0.594523   0.312277
+ 1.04   0.838572    0.631878   0.429465      0.812342    0.737151   0.5053
+ 1.08   0.166221    0.29205    0.244582      0.363678    0.801242   0.328169
+ 1.12   0.11787     0.371583   0.400001      0.92906     0.337296   0.760043
  ⋮                                       ⋱               ⋮
- 1.84   0.880853    0.578329   0.394576      0.255579    0.750947   0.27206
- 1.88   0.363918    0.533041   0.545522      0.516268    0.531012   0.503183
- 1.92   0.259597    0.358535   0.748884      0.575727    0.227865   0.865713
- 1.96   0.34589     0.94196    0.657054  …   0.728852    0.462859   0.767151
- 2.0    0.0143204   0.985407   0.476387      0.0417586   0.286      0.777391
And we can simply select values from B with selectors from A:
julia
julia> B[DimSelectors(A)]
╭─────────────────────────╮
+ 1.84   0.528009    0.951796   0.188707      0.0411113   0.608263   0.993001
+ 1.88   0.229792    0.565165   0.910836      0.162784    0.410125   0.699118
+ 1.92   0.574678    0.618541   0.187648      0.952744    0.583476   0.460775
+ 1.96   0.89559     0.812106   0.953145  …   0.727405    0.559465   0.788252
+ 2.0    0.595409    0.630782   0.298791      0.391258    0.320737   0.216905
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   0.727464   0.712342    0.14203    0.207735    0.677543   0.300304
- 1.2   0.815253   0.642187    0.49973    0.0387029   0.319496   0.887979
- 1.4   0.391307   0.197423    0.166425   0.48124     0.300285   0.0574938
- 1.6   0.49224    0.795274    0.24625    0.735391    0.699976   0.973586
- 1.8   0.539197   0.0661958   0.811917   0.822008    0.848624   0.930699
- 2.0   0.777391   0.0417586   0.873218   0.834418    0.476387   0.0143204
If the lookups aren't aligned we can use Near instead of At, which like doing a nearest neighbor interpolation:
julia
julia> C = rand(X(1.0:0.007:2.0), Y(10.0:0.9:30))
╭────────────────────────────╮
+ ↓ →  10         12         14          16          18          20
+ 1.0   0.312277   0.746037   0.0623229   0.930614    0.898833    0.0775482
+ 1.2   0.825676   0.208304   0.232648    0.856029    0.649595    0.495624
+ 1.4   0.87475    0.403556   0.0990865   0.571126    0.0564029   0.909826
+ 1.6   0.939546   0.097066   0.139173    0.0566306   0.833475    0.0831822
+ 1.8   0.747695   0.721253   0.998253    0.781923    0.554833    0.228081
+ 2.0   0.216905   0.391258   0.719849    0.127621    0.298791    0.595409
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     0.168617     0.417756       0.258764    0.195897    0.747615
- 1.007   0.395053     0.994295       0.770101    0.403836    0.891596
- 1.014   0.144422     0.370454       0.878565    0.753744    0.0149561
- 1.021   0.938771     0.917143       0.679296    0.180397    0.436168
- ⋮                               ⋱
- 1.966   0.454047     0.92977        0.662833    0.160403    0.276742
- 1.973   0.846736     0.0923954  …   0.43915     0.405912    0.245638
- 1.98    0.946655     0.0506805      0.0821229   0.0787968   0.613895
- 1.987   0.00686195   0.266013       0.485952    0.193179    0.0628947
- 1.994   0.84475      0.574619       0.0760101   0.950007    0.662413
julia
julia> C[DimSelectors(A; selectors=Near)]
╭─────────────────────────╮
+ ↓ →    10.0       10.9       11.8        …  28.0       28.9       29.8
+ 1.0     0.412631   0.707629   0.640577       0.329029   0.945549   0.505129
+ 1.007   0.300304   0.66424    0.948532       0.565649   0.27236    0.782344
+ 1.014   0.421525   0.564636   0.267693       0.861783   0.53422    0.906871
+ 1.021   0.399781   0.381315   0.970303       0.449093   0.560553   0.565202
+ ⋮                                        ⋱
+ 1.966   0.608086   0.605375   0.336929       0.891637   0.537974   0.509288
+ 1.973   0.500679   0.179953   0.183503   …   0.851044   0.206886   0.824656
+ 1.98    0.673494   0.613381   0.0136364      0.427523   0.397621   0.742451
+ 1.987   0.294489   0.949143   0.520037       0.307031   0.18378    0.531598
+ 1.994   0.725774   0.525431   0.870641       0.919448   0.09814    0.331422
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     0.168617    0.397386    0.133475    0.0630398   0.247976   0.145293
- 1.203   0.233685    0.243195    0.331147    0.584048    0.717841   0.832538
- 1.399   0.0164892   0.729706    0.0315018   0.899356    0.847872   0.358513
- 1.602   0.0426808   0.61781     0.808889    0.140817    0.188976   0.054083
- 1.798   0.226699    0.0983553   0.661444    0.750622    0.650842   0.986606
- 1.994   0.84475     0.366873    0.734751    0.590953    0.621527   0.23502
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
+ ↓ →    10.0        11.8       13.6       16.3        18.1       19.9
+ 1.0     0.412631    0.640577   0.505726   0.371838    0.49487    0.0639461
+ 1.203   0.824659    0.913321   0.510752   0.29488     0.154653   0.647062
+ 1.399   0.366634    0.653364   0.568727   0.925888    0.660823   0.645043
+ 1.602   0.0515532   0.324073   0.424831   0.808144    0.838211   0.71953
+ 1.798   0.205861    0.414017   0.453507   0.526825    0.180915   0.125626
+ 1.994   0.725774    0.870641   0.484227   0.0294193   0.6675     0.712805
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
diff --git a/dev/assets/stacks.md.SmvHWrk4.js b/dev/assets/stacks.md.DhVQ6mRN.js
similarity index 90%
rename from dev/assets/stacks.md.SmvHWrk4.js
rename to dev/assets/stacks.md.DhVQ6mRN.js
index 9586148b9..cb668b0d2 100644
--- a/dev/assets/stacks.md.SmvHWrk4.js
+++ b/dev/assets/stacks.md.DhVQ6mRN.js
@@ -1,4 +1,4 @@
-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.B8l2DGq3.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
+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.BAAK0EyE.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)))
╭───────────────╮
@@ -11,33 +11,33 @@ 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"./ch
   :b eltype: Float64 dims: X, Y size: 10×6
   :c eltype: Float64 dims: Y size: 6
   :d eltype: Float64 dims: X size: 10
-└──────────────────────────────────────────────────────────────────────────────┘
The behaviour of a DimStack is at times like a NamedTuple of DimArray and, others 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(`
+└──────────────────────────────────────────────────────────────────────────────┘
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(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"│ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Float64,2}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," a"),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(`
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-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  2.0"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  0.386306  0.668226  0.278077   0.52735    0.775634    0.440884")]),s(`
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-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.7140841681506571, b = 0.13525245310587153, c = 0.4802202427553709, d = 0.5663088629322468)")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.15055904027841316, b = 0.19122366809754, c = 0.4802202427553709, d = 0.06317733925981239)")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.9130495399200352, b = 0.9436941638102098, c = 0.4802202427553709, d = 0.9919365440188083)")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.938732432349465, b = 0.17715541586710937, c = 0.4802202427553709, d = 0.5514523568721104)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t('
Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
',3)),h(e,null,{default:l(()=>[h(k,{label:"maximum"},{default:l(()=>a[8]||(a[8]=[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"}}," maximum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st)")])])])],-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"}},"(a = 0.9698812177371097, b = 0.9982064541308482, c = 0.4802202427553709, d = 0.9919365440188083)")])])])],-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"}}," maximum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st; dims"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"Y)")])])])],-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":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.287284213573217, b = 0.8002301328044032, c = 0.5762235094109146, d = 0.8030281996030815)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.47743850127749476, b = 0.5370455292538254, c = 0.5762235094109146, d = 0.006378200703587189)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.048092019187169766, b = 0.7814968833366662, c = 0.5762235094109146, d = 0.2868262581079416)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.08783435714630261, b = 0.4408839264303761, c = 0.5762235094109146, d = 0.7802406914680406)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.6206571460949776, b = 0.5203602669427675, c = 0.5762235094109146, d = 0.5476725802849189)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.7041047189542633, b = 0.7057009741792366, c = 0.5762235094109146, d = 0.6672884952890055)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5746233955742341, b = 0.3517350991207968, c = 0.5762235094109146, d = 0.05946710667513877)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5794916001308447, b = 0.6691920691407321, c = 0.5762235094109146, d = 0.17715541586710937)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5624913882274706, b = 0.7140841681506571, c = 0.5762235094109146, d = 0.04060837507113502)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t('
Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
',3)),h(e,null,{default:l(()=>[h(k,{label:"maximum"},{default:l(()=>a[8]||(a[8]=[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"}}," maximum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st)")])])])],-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"}},"(a = 0.9708243398463525, b = 0.97164033242648, c = 0.938732432349465, d = 0.8030281996030815)")])])])],-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"}}," maximum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st; dims"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"Y)")])])])],-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"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimStack"),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"}},"1.0:1.0:10.0"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered"),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(`
@@ -134,7 +134,7 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),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"}},"Y"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1")]),s(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :c"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"Y"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1")]),s(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :d"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),h(k,{label:"minimum"},{default:l(()=>a[9]||(a[9]=[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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st)")])])])],-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"}},"(a = 0.010277581356302745, b = 0.006378200703587189, c = 0.015759978623873905, d = 0.06317733925981239)")])])])],-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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st; dims"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"Y)")])])])],-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"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),h(k,{label:"minimum"},{default:l(()=>a[9]||(a[9]=[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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st)")])])])],-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"}},"(a = 0.02514568569664144, b = 0.0038728108200783984, c = 0.15055904027841316, d = 0.006378200703587189)")])])])],-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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st; dims"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"Y)")])])])],-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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 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"├───────────────┴──────────────────────────────────────────────────────── dims ┐")]),s(`
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@@ -233,7 +233,7 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10")]),s(`
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@@ -361,8 +361,8 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"Y"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"10")]),s(`
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-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[29]||(a[29]=t(`
Performance ​
Indexing stack is fast - indexing a single value return a NamedTuple from all layers is usually measures 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
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[29]||(a[29]=t(`
Performance ​
Indexing a stack is fast - indexing a single value and returning a NamedTuple 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]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
+julia> @btime $st[X=1, Y=4]
  4.067 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
julia
julia> @btime $st[1, 4]
  4.038 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
diff --git a/dev/assets/stacks.md.SmvHWrk4.lean.js b/dev/assets/stacks.md.DhVQ6mRN.lean.js
similarity index 90%
rename from dev/assets/stacks.md.SmvHWrk4.lean.js
rename to dev/assets/stacks.md.DhVQ6mRN.lean.js
index 9586148b9..cb668b0d2 100644
--- a/dev/assets/stacks.md.SmvHWrk4.lean.js
+++ b/dev/assets/stacks.md.DhVQ6mRN.lean.js
@@ -1,4 +1,4 @@
-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.B8l2DGq3.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
+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.BAAK0EyE.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)))
╭───────────────╮
@@ -11,33 +11,33 @@ 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"./ch
   :b eltype: Float64 dims: X, Y size: 10×6
   :c eltype: Float64 dims: Y size: 6
   :d eltype: Float64 dims: X size: 10
-└──────────────────────────────────────────────────────────────────────────────┘
The behaviour of a DimStack is at times like a NamedTuple of DimArray and, others 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(`
+└──────────────────────────────────────────────────────────────────────────────┘
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(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"│ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Float64,2}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," a"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," │")]),s(`
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Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.287284213573217, b = 0.8002301328044032, c = 0.5762235094109146, d = 0.8030281996030815)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.47743850127749476, b = 0.5370455292538254, c = 0.5762235094109146, d = 0.006378200703587189)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.048092019187169766, b = 0.7814968833366662, c = 0.5762235094109146, d = 0.2868262581079416)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.08783435714630261, b = 0.4408839264303761, c = 0.5762235094109146, d = 0.7802406914680406)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.6206571460949776, b = 0.5203602669427675, c = 0.5762235094109146, d = 0.5476725802849189)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.7041047189542633, b = 0.7057009741792366, c = 0.5762235094109146, d = 0.6672884952890055)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5746233955742341, b = 0.3517350991207968, c = 0.5762235094109146, d = 0.05946710667513877)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5794916001308447, b = 0.6691920691407321, c = 0.5762235094109146, d = 0.17715541586710937)")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," (a = 0.5624913882274706, b = 0.7140841681506571, c = 0.5762235094109146, d = 0.04060837507113502)")])])])],-1)])),_:1})]),_:1}),a[27]||(a[27]=t('
Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
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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"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimStack"),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"}},"1.0:1.0:10.0"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered"),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(`
@@ -134,7 +134,7 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),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"}},"Y"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"1")]),s(`
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-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),h(k,{label:"minimum"},{default:l(()=>a[9]||(a[9]=[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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st)")])])])],-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"}},"(a = 0.010277581356302745, b = 0.006378200703587189, c = 0.015759978623873905, d = 0.06317733925981239)")])])])],-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"}}," minimum"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st; dims"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"Y)")])])])],-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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@@ -144,7 +144,7 @@ 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"./ch
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 `),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"}},"1.0:1.0:10.0"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered"),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(`
@@ -233,7 +233,7 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10")]),s(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :c"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: ")]),s(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :d"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[28]||(a[28]=i("p",null,[i("code",null,"broadcast_dims"),s(" broadcasts functions over any mix of "),i("code",null,"AbstractDimStack"),s(" and "),i("code",null,"AbstractDimArray"),s(" returning a new "),i("code",null,"AbstractDimStack"),s(" with layers the size of the largest layer in the broadcast. This will work even if dimension permutation does not match in the objects.")],-1)),h(e,null,{default:l(()=>[h(k,{label:"rotl90"},{default:l(()=>a[18]||(a[18]=[i("p",null,"Only matrix layers can be rotated",-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"}}," rotl90"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st[("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":a"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":b"),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"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[28]||(a[28]=i("p",null,[i("a",{href:"/DimensionalData.jl/dev/api/reference#DimensionalData.broadcast_dims"},[i("code",null,"broadcast_dims")]),s(" broadcasts functions over any mix of "),i("code",null,"AbstractDimStack"),s(" and "),i("code",null,"AbstractDimArray"),s(" returning a new "),i("code",null,"AbstractDimStack"),s(" with layers the size of the largest layer in the broadcast. This will work even if dimension permutation does not match in the objects.")],-1)),h(e,null,{default:l(()=>[h(k,{label:"rotl90"},{default:l(()=>a[18]||(a[18]=[i("p",null,"Only matrix layers can be rotated",-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"}}," rotl90"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(st[("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":a"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":b"),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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@@ -361,8 +361,8 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}},"  :b"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," eltype: "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"Float64"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," dims: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"Y"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"X"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," size: "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"6"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"10")]),s(`
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-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[29]||(a[29]=t(`
Performance ​
Indexing stack is fast - indexing a single value return a NamedTuple from all layers is usually measures 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
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),a[29]||(a[29]=t(`
Performance ​
Indexing a stack is fast - indexing a single value and returning a NamedTuple 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]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
+julia> @btime $st[X=1, Y=4]
  4.067 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
julia
julia> @btime $st[1, 4]
  4.038 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
diff --git a/dev/assets/style.CaBzy52z.css b/dev/assets/style.BjYx-k93.css
similarity index 97%
rename from dev/assets/style.CaBzy52z.css
rename to dev/assets/style.BjYx-k93.css
index ebdf959e5..2c2c3b350 100644
--- a/dev/assets/style.CaBzy52z.css
+++ b/dev/assets/style.BjYx-k93.css
@@ -1 +1 @@
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"Menlo", "Monaco", "Consolas", "Liberation Mono", "Courier New", monospace;font-optical-sizing:auto}:root:where(:lang(zh)){--vp-font-family-base: "Punctuation SC", "Inter", ui-sans-serif, system-ui, "PingFang SC", "Noto Sans CJK SC", "Noto Sans SC", "Heiti SC", "Microsoft YaHei", "DengXian", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"}:root{--vp-shadow-1: 0 1px 2px rgba(0, 0, 0, .04), 0 1px 2px rgba(0, 0, 0, .06);--vp-shadow-2: 0 3px 12px rgba(0, 0, 0, .07), 0 1px 4px rgba(0, 0, 0, .07);--vp-shadow-3: 0 12px 32px rgba(0, 0, 0, .1), 0 2px 6px rgba(0, 0, 0, .08);--vp-shadow-4: 0 14px 44px rgba(0, 0, 0, .12), 0 3px 9px rgba(0, 0, 0, .12);--vp-shadow-5: 0 18px 56px rgba(0, 0, 0, .16), 0 4px 12px rgba(0, 0, 0, .16)}:root{--vp-z-index-footer: 10;--vp-z-index-local-nav: 20;--vp-z-index-nav: 30;--vp-z-index-layout-top: 40;--vp-z-index-backdrop: 50;--vp-z-index-sidebar: 60}@media (min-width: 960px){:root{--vp-z-index-sidebar: 25}}:root{--vp-layout-max-width: 1440px}:root{--vp-header-anchor-symbol: "#"}:root{--vp-code-line-height: 1.7;--vp-code-font-size: .875em;--vp-code-color: var(--vp-c-brand-1);--vp-code-link-color: var(--vp-c-brand-1);--vp-code-link-hover-color: var(--vp-c-brand-2);--vp-code-bg: var(--vp-c-default-soft);--vp-code-block-color: var(--vp-c-text-2);--vp-code-block-bg: var(--vp-c-bg-alt);--vp-code-block-divider-color: var(--vp-c-gutter);--vp-code-lang-color: var(--vp-c-text-3);--vp-code-line-highlight-color: var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: transparent;--vp-button-sponsor-active-border: var(--vp-c-sponsor);--vp-button-sponsor-active-text: var(--vp-c-sponsor);--vp-button-sponsor-active-bg: transparent}:root{--vp-custom-block-font-size: 14px;--vp-custom-block-code-font-size: 13px;--vp-custom-block-info-border: transparent;--vp-custom-block-info-text: var(--vp-c-text-1);--vp-custom-block-info-bg: var(--vp-c-default-soft);--vp-custom-block-info-code-bg: var(--vp-c-default-soft);--vp-custom-block-note-border: transparent;--vp-custom-block-note-text: var(--vp-c-text-1);--vp-custom-block-note-bg: var(--vp-c-default-soft);--vp-custom-block-note-code-bg: var(--vp-c-default-soft);--vp-custom-block-tip-border: transparent;--vp-custom-block-tip-text: var(--vp-c-text-1);--vp-custom-block-tip-bg: var(--vp-c-tip-soft);--vp-custom-block-tip-code-bg: var(--vp-c-tip-soft);--vp-custom-block-important-border: transparent;--vp-custom-block-important-text: var(--vp-c-text-1);--vp-custom-block-important-bg: var(--vp-c-important-soft);--vp-custom-block-important-code-bg: var(--vp-c-important-soft);--vp-custom-block-warning-border: transparent;--vp-custom-block-warning-text: var(--vp-c-text-1);--vp-custom-block-warning-bg: var(--vp-c-warning-soft);--vp-custom-block-warning-code-bg: var(--vp-c-warning-soft);--vp-custom-block-danger-border: transparent;--vp-custom-block-danger-text: var(--vp-c-text-1);--vp-custom-block-danger-bg: var(--vp-c-danger-soft);--vp-custom-block-danger-code-bg: var(--vp-c-danger-soft);--vp-custom-block-caution-border: transparent;--vp-custom-block-caution-text: var(--vp-c-text-1);--vp-custom-block-caution-bg: var(--vp-c-caution-soft);--vp-custom-block-caution-code-bg: var(--vp-c-caution-soft);--vp-custom-block-details-border: var(--vp-custom-block-info-border);--vp-custom-block-details-text: var(--vp-custom-block-info-text);--vp-custom-block-details-bg: var(--vp-custom-block-info-bg);--vp-custom-block-details-code-bg: var(--vp-custom-block-info-code-bg)}:root{--vp-input-border-color: var(--vp-c-border);--vp-input-bg-color: var(--vp-c-bg-alt);--vp-input-switch-bg-color: var(--vp-c-default-soft)}:root{--vp-nav-height: 64px;--vp-nav-bg-color: var(--vp-c-bg);--vp-nav-screen-bg-color: var(--vp-c-bg);--vp-nav-logo-height: 24px}.hide-nav{--vp-nav-height: 0px}.hide-nav .VPSidebar{--vp-nav-height: 22px}:root{--vp-local-nav-bg-color: var(--vp-c-bg)}:root{--vp-sidebar-width: 272px;--vp-sidebar-bg-color: var(--vp-c-bg-alt)}:root{--vp-backdrop-bg-color: rgba(0, 0, 0, .6)}:root{--vp-home-hero-name-color: var(--vp-c-brand-1);--vp-home-hero-name-background: transparent;--vp-home-hero-image-background-image: none;--vp-home-hero-image-filter: none}:root{--vp-badge-info-border: transparent;--vp-badge-info-text: var(--vp-c-text-2);--vp-badge-info-bg: var(--vp-c-default-soft);--vp-badge-tip-border: transparent;--vp-badge-tip-text: var(--vp-c-tip-1);--vp-badge-tip-bg: var(--vp-c-tip-soft);--vp-badge-warning-border: transparent;--vp-badge-warning-text: var(--vp-c-warning-1);--vp-badge-warning-bg: var(--vp-c-warning-soft);--vp-badge-danger-border: transparent;--vp-badge-danger-text: var(--vp-c-danger-1);--vp-badge-danger-bg: var(--vp-c-danger-soft)}:root{--vp-carbon-ads-text-color: var(--vp-c-text-1);--vp-carbon-ads-poweredby-color: var(--vp-c-text-2);--vp-carbon-ads-bg-color: var(--vp-c-bg-soft);--vp-carbon-ads-hover-text-color: var(--vp-c-brand-1);--vp-carbon-ads-hover-poweredby-color: var(--vp-c-text-1)}:root{--vp-local-search-bg: var(--vp-c-bg);--vp-local-search-result-bg: var(--vp-c-bg);--vp-local-search-result-border: var(--vp-c-divider);--vp-local-search-result-selected-bg: var(--vp-c-bg);--vp-local-search-result-selected-border: var(--vp-c-brand-1);--vp-local-search-highlight-bg: var(--vp-c-brand-1);--vp-local-search-highlight-text: var(--vp-c-neutral-inverse)}@media (prefers-reduced-motion: 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diff --git a/dev/assets/tables.md.t8vBJGld.js b/dev/assets/tables.md.C58ZkjII.js
similarity index 86%
rename from dev/assets/tables.md.t8vBJGld.js
rename to dev/assets/tables.md.C58ZkjII.js
index 295501fa3..a4500e654 100644
--- a/dev/assets/tables.md.t8vBJGld.js
+++ b/dev/assets/tables.md.C58ZkjII.js
@@ -1,8 +1,8 @@
-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.B8l2DGq3.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, giving 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 or the array or the stack layer key. Dimension columns use the Symbol version (the result of DD.name(dimension)).
Looping of dimensions and stack layers is done lazily, and does not allocate unless collected.
Example ​
julia
using DimensionalData
+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.BAAK0EyE.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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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}},"    6 │ (6, 1)    a         0.192192   0.489525")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}},"    7 │ (7, 1)    a         0.607291   0.793832")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}},"    8 │ (8, 1)    a         0.921958   0.00191986")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}},"  ⋮   │    ⋮         ⋮          ⋮          ⋮")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2594 │ (4, 10)   z         0.107233   0.963013")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2595 │ (5, 10)   z         0.934189   0.82194")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2596 │ (6, 10)   z         0.294486   0.368519")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2597 │ (7, 10)   z         0.616331   0.233504")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2598 │ (8, 10)   z         0.0201588  0.795927")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2599 │ (9, 10)   z         0.334393   0.377799")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark":"#959da5","--shiki-dark-font-weight":"bold"}}," 2600 │ (10, 10)  z         0.485876   0.2276")]),s(`
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#1b7c83","--shiki-light-font-weight":"bold","--shiki-dark":"#39c5cf","--shiki-dark-font-weight":"bold"}},"                                 2585 rows omitted")])])])],-1)])),_:1})]),_:1}),a[8]||(a[8]=e(`
Converting to CSV ​
We can also write arrays and stacks directly to CSV.jl, or any other data type supporting the Tables.jl interface.
julia
using CSV
 CSV.write("dimstack.csv", st)
 readlines("dimstack.csv")
2601-element Vector{String}:
  "X,Y,category,data1,data2"
- "1,1,a,0.5501481631111826,0.9447511416331498"
- "2,1,a,0.09300753748828394,0.15945803739833375"
- "3,1,a,0.48952511607945026,0.6146564273146751"
- "4,1,a,0.7938317326707394,0.9770663775826343"
- "5,1,a,0.0019198597596568057,0.798655984630017"
- "6,1,a,0.8612776980335002,0.6730273352488979"
- "7,1,a,0.20758428874582302,0.7683418213117802"
- "8,1,a,0.023646798570656102,0.012300334322905893"
- "9,1,a,0.11925244363082943,0.4278264986513013"
+ "1,1,a,0.4473266106898912,0.55560637324799"
+ "2,1,a,0.9655293490481315,0.10276733254788795"
+ "3,1,a,0.48748811097285394,0.22237128922242078"
+ "4,1,a,0.2674330482715843,0.5501481631111826"
+ "5,1,a,0.5992407552660244,0.09300753748828394"
+ "6,1,a,0.19219227965820063,0.48952511607945026"
+ "7,1,a,0.6072910004472037,0.7938317326707394"
+ "8,1,a,0.9219584479428687,0.0019198597596568057"
+ "9,1,a,0.9217489713778885,0.44833963865079907"
  ⋮
- "2,10,z,0.8146121812750928,0.3434655288098666"
- "3,10,z,0.38167574879167476,0.24524306337289326"
- "4,10,z,0.8719143923648308,0.1985699519321249"
- "5,10,z,0.5040228055200978,0.694278906020718"
- "6,10,z,0.35100608350331053,0.20636222545147498"
- "7,10,z,0.8560905731682101,0.8428656510212863"
- "8,10,z,0.04599641808658339,0.6150940338022266"
- "9,10,z,0.005163666306917225,0.768924169642427"
- "10,10,z,0.8288995877624121,0.8136963000324107"
`,4))])}const E=n(r,[["render",f]]);export{m as __pageData,E as default};
+ "2,10,z,0.39189449474581917,0.42429962356614626"
+ "3,10,z,0.21066413424755615,0.836907965319872"
+ "4,10,z,0.10723267004015569,0.9630127530566065"
+ "5,10,z,0.9341886269251364,0.8219403925584018"
+ "6,10,z,0.29448593792551514,0.36851882799081104"
+ "7,10,z,0.6163310918833074,0.23350386812772128"
+ "8,10,z,0.020158765333216477,0.7959265671836858"
+ "9,10,z,0.3343926384599254,0.3777991041100621"
+ "10,10,z,0.4858762080349691,0.2276004407628871"
`,4))])}const E=n(r,[["render",f]]);export{m as __pageData,E as default};
diff --git a/dev/assets/tables.md.t8vBJGld.lean.js b/dev/assets/tables.md.C58ZkjII.lean.js
similarity index 86%
rename from dev/assets/tables.md.t8vBJGld.lean.js
rename to dev/assets/tables.md.C58ZkjII.lean.js
index 295501fa3..a4500e654 100644
--- a/dev/assets/tables.md.t8vBJGld.lean.js
+++ b/dev/assets/tables.md.C58ZkjII.lean.js
@@ -1,8 +1,8 @@
-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.B8l2DGq3.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, giving 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 or the array or the stack layer key. Dimension columns use the Symbol version (the result of DD.name(dimension)).
Looping of dimensions and stack layers is done lazily, and does not allocate unless collected.
Example ​
julia
using DimensionalData
+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.BAAK0EyE.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')
`,10)),h(k,null,{default:t(()=>[h(l,{label:"create a `DimArray`"},{default:t(()=>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"}},"(x, y, c; name"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":data"),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(`
+↗ category 'a':1:'z')
`,10)),h(k,null,{default:t(()=>[h(l,{label:"Create a `DimArray`"},{default:t(()=>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"}},"(x, y, c; name"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":data"),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"}},"10"),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"}},"×"),i("span",{style:{"--shiki-light":"#5fd7ff","--shiki-dark":"#5fd7ff"}},"26"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Float64,3}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," data"),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{Int64} "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"1:10"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered"),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(`
@@ -10,16 +10,16 @@ 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"./ch
 `),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#5fd7ff","--shiki-dark":"#5fd7ff"}},"  ↗ "),i("span",{style:{"--shiki-light":"#5fd7ff","--shiki-dark":"#5fd7ff"}},"category"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Categorical{Char} "),i("span",{style:{"--shiki-light":"#5fd7ff","--shiki-dark":"#5fd7ff"}},"'a':1:'z'"),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":"#24292e","--shiki-dark":"#e1e4e8"}},"["),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},":"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},":"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#5fd7ff","--shiki-dark":"#5fd7ff"}},"1"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"]")]),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"}},"  1"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"         2"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"         3"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"           …  "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"8"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"          9"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"         10")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  1"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"    0.921958  0.128827  0.517175       0.947312   0.743407   0.0120967")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  2"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"    0.449491  0.176024  0.914911       0.674061   0.291555   0.266342")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  3"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"    0.581131  0.436605  0.694066       0.0325131  0.645678   0.620473")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  4"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"    0.194849  0.580091  0.176766       0.888721   0.890574   0.985377")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"  ⋮                                    ⋱                        ⋮")]),s(`
-`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"  7"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"    0.191934  0.359421  0.51621        0.828161   0.516313   0.74172")]),s(`
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Converting to CSV ​
We can also write arrays and stacks directly to CSV.jl, or any other data type supporting the Tables.jl interface.
julia
using CSV
 CSV.write("dimstack.csv", st)
 readlines("dimstack.csv")
2601-element Vector{String}:
  "X,Y,category,data1,data2"
- "1,1,a,0.5501481631111826,0.9447511416331498"
- "2,1,a,0.09300753748828394,0.15945803739833375"
- "3,1,a,0.48952511607945026,0.6146564273146751"
- "4,1,a,0.7938317326707394,0.9770663775826343"
- "5,1,a,0.0019198597596568057,0.798655984630017"
- "6,1,a,0.8612776980335002,0.6730273352488979"
- "7,1,a,0.20758428874582302,0.7683418213117802"
- "8,1,a,0.023646798570656102,0.012300334322905893"
- "9,1,a,0.11925244363082943,0.4278264986513013"
+ "1,1,a,0.4473266106898912,0.55560637324799"
+ "2,1,a,0.9655293490481315,0.10276733254788795"
+ "3,1,a,0.48748811097285394,0.22237128922242078"
+ "4,1,a,0.2674330482715843,0.5501481631111826"
+ "5,1,a,0.5992407552660244,0.09300753748828394"
+ "6,1,a,0.19219227965820063,0.48952511607945026"
+ "7,1,a,0.6072910004472037,0.7938317326707394"
+ "8,1,a,0.9219584479428687,0.0019198597596568057"
+ "9,1,a,0.9217489713778885,0.44833963865079907"
  ⋮
- "2,10,z,0.8146121812750928,0.3434655288098666"
- "3,10,z,0.38167574879167476,0.24524306337289326"
- "4,10,z,0.8719143923648308,0.1985699519321249"
- "5,10,z,0.5040228055200978,0.694278906020718"
- "6,10,z,0.35100608350331053,0.20636222545147498"
- "7,10,z,0.8560905731682101,0.8428656510212863"
- "8,10,z,0.04599641808658339,0.6150940338022266"
- "9,10,z,0.005163666306917225,0.768924169642427"
- "10,10,z,0.8288995877624121,0.8136963000324107"
`,4))])}const E=n(r,[["render",f]]);export{m as __pageData,E as default};
+ "2,10,z,0.39189449474581917,0.42429962356614626"
+ "3,10,z,0.21066413424755615,0.836907965319872"
+ "4,10,z,0.10723267004015569,0.9630127530566065"
+ "5,10,z,0.9341886269251364,0.8219403925584018"
+ "6,10,z,0.29448593792551514,0.36851882799081104"
+ "7,10,z,0.6163310918833074,0.23350386812772128"
+ "8,10,z,0.020158765333216477,0.7959265671836858"
+ "9,10,z,0.3343926384599254,0.3777991041100621"
+ "10,10,z,0.4858762080349691,0.2276004407628871"
`,4))])}const E=n(r,[["render",f]]);export{m as __pageData,E as default};
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--- a/dev/basics.html
+++ b/dev/basics.html
@@ -5,14 +5,14 @@
     
     DimensionalData.jl
     
-    
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@@ -20,7 +20,7 @@
     
   
   
-    
DimensionalData.jl
Appearance
Installation ​
If you want to use this package you need to install it first. You can do it using the following commands:
julia
julia> ] # ']' should be pressed
+    
DimensionalData.jl
Appearance
Installation ​
If you want to use this package you need to install it first. You can do it using the following commands:
julia
julia> ] # ']' should be pressed
 pkg> add DimensionalData
or
julia
julia> using Pkg
 julia> Pkg.add("DimensionalData")
Additionally, it is recommended to check the version that you have installed with the status command.
julia
julia> ]
 pkg> status DimensionalData
Basics ​
Start using the package:
julia
using DimensionalData
and create your first DimArray
julia
julia> A = DimArray(rand(4,5), (a=1:4, b=1:5))
╭─────────────────────────╮
@@ -29,36 +29,36 @@
   ↓ a Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → b Sampled{Int64} 1:5 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- ↓ →  1         2          3         4         5
- 1    0.541806  0.0282382  0.762794  0.844547  0.919763
- 2    0.300768  0.247683   0.388243  0.95636   0.383632
- 3    0.70597   0.599558   0.803192  0.675435  0.487578
- 4    0.93479   0.624685   0.774731  0.216524  0.102405
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
+ ↓ →  1          2         3          4          5
+ 1    0.0333106  0.758203  0.973154   0.0710221  0.868064
+ 2    0.52849    0.571821  0.920182   0.545156   0.106593
+ 3    0.70366    0.539028  0.0670111  0.404635   0.799977
+ 4    0.68316    0.965008  0.0781957  0.130775   0.570914
or
julia
julia> C = DimArray(rand(Int8, 10), (alpha='a':'j',))
╭─────────────────────────────╮
 │ 10-element DimArray{Int8,1} │
 ├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ alpha Categorical{Char} 'a':1:'j' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
- 'a'  112
- 'b'   85
- 'c'  -86
- 'd'  123
- 'e'   95
- 'f'   59
- 'g'  -91
- 'h'   55
- 'i'  -78
- 'j'  -15
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
+ 'a'  -21
+ 'b'  101
+ 'c'   51
+ 'd'  -35
+ 'e'  106
+ 'f'   88
+ 'g'  -98
+ 'h'   14
+ 'i'  -73
+ 'j'  -54
or something a little bit more complicated:
julia
julia> data = rand(Int8, 2, 10, 3) .|> abs
2×10×3 Array{Int8, 3}:
 [:, :, 1] =
-  65  110   71  108  76  15  120   65  121  126
- 125   78  118  101  11  32   87  108   49  115
+ 46  76  38  32  120  122  29  57   9  23
+ 78  26   6  50   16  125  11  98  39  95
 
 [:, :, 2] =
- 106  84  74  80   9   26  35  74  28  47
-  87   1  42  54  15  107  77  23  89  34
+  50  101  46  48  76  17    1   69  11  112
+ 105   15  50  90   5  70  107  120  86   15
 
 [:, :, 3] =
- 65  66  113  1  123  16  69  104  -128  26
- 25  13   29  1   21  72  24   48    51  88
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
+ 38  59   41  47  41   11  117   82   74   61
+ 76  72  112  89  39  109   86  117  117  120
julia
julia> B = DimArray(data, (channel=[:left, :right], time=1:10, iter=1:3))
╭─────────────────────────╮
 │ 2×10×3 DimArray{Int8,3} │
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
   ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
@@ -66,10 +66,10 @@
   ↗ iter    Sampled{Int64} 1:3 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →        1    2    3    4   5   6    7    8    9   10
-  :left    65  110   71  108  76  15  120   65  121  126
-  :right  125   78  118  101  11  32   87  108   49  115

-    
+ ↓ →       1   2   3   4    5    6   7   8   9  10
+  :left   46  76  38  32  120  122  29  57   9  23
+  :right  78  26   6  50   16  125  11  98  39  95

+    
     
   
 
\ No newline at end of file
diff --git a/dev/broadcasts.html b/dev/broadcasts.html
index ca4b50aa2..bc6f6034b 100644
--- a/dev/broadcasts.html
+++ b/dev/broadcasts.html
@@ -5,14 +5,14 @@
     
     Dimensional broadcasts with @d and broadcast_dims | DimensionalData.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -20,7 +20,7 @@
     
   
   
-    
DimensionalData.jl
Appearance
Dimensional broadcasts with @d and broadcast_dims ​
Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with eachother, and that values match. Strict checks can be turned of globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).
The @d macro is a dimension-aware extension to regular dot brodcasting. broadcast_dims and broadcast_dims are analagous to Base julia broadcast.
Because we know the names of the dimensions, there is no ambiguity in which one we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.
As an added bonus, broadcast_dims even works on DimStacks. Currently @d does not work on DimStack.
Example: scaling along the time dimension ​
Define some dimensions:
julia
using DimensionalData
+    
DimensionalData.jl
Appearance
Dimensional broadcasts with @d and broadcast_dims ​
Broadcasting over AbstractDimArray works as usual with Base Julia broadcasts, except that dimensions are checked for compatibility with each other, and that values match. Strict checks can be turned off globally with strict_broadcast!(false). To avoid even dimension name checks, broadcast over parent(dimarray).
The @d macro is a dimension-aware extension to regular dot broadcasting. broadcast_dims is analogous to Base Julia's broadcast.
Because we know the names of the dimensions, there is no ambiguity in which ones we mean to broadcast together. This means we can permute and reshape dims so that broadcasts that would fail with a regular Array just work with a DimArray.
As an added bonus, broadcast_dims even works on DimStacks. Currently, @d does not work on DimStack.
Example: scaling along the time dimension ​
Define some dimensions:
julia
using DimensionalData
 using Dates
 using Statistics
julia
julia> x, y, t = X(1:100), Y(1:25), Ti(DateTime(2000):Month(1):DateTime(2000, 12))
(↓ X  1:100,
 → Y  1:25,
@@ -48,16 +48,16 @@
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size; got a dimension with lengths 100 and 12
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
A regular broadcast fails:
julia
julia> scaled = data .* month_scalars
ERROR: DimensionMismatch: arrays could not be broadcast to a common size: a has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(100), X{Sampled{Int64, UnitRange{Int64}, ForwardOrdered, Regular{Int64}, Points, NoMetadata}}([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100])) and b has axes DimensionalData.Dimensions.DimUnitRange(Base.OneTo(12), Ti{Sampled{Dates.DateTime, StepRange{Dates.DateTime, Dates.Month}, ForwardOrdered, Regular{Dates.Month}, Points, NoMetadata}}([Dates.DateTime("2000-01-01T00:00:00"), Dates.DateTime("2000-02-01T00:00:00"), Dates.DateTime("2000-03-01T00:00:00"), Dates.DateTime("2000-04-01T00:00:00"), Dates.DateTime("2000-05-01T00:00:00"), Dates.DateTime("2000-06-01T00:00:00"), Dates.DateTime("2000-07-01T00:00:00"), Dates.DateTime("2000-08-01T00:00:00"), Dates.DateTime("2000-09-01T00:00:00"), Dates.DateTime("2000-10-01T00:00:00"), Dates.DateTime("2000-11-01T00:00:00"), Dates.DateTime("2000-12-01T00:00:00")]))
But @d knows to broadcast over the Ti dimension:
julia
julia> scaled = @d data .* month_scalars
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -65,48 +65,48 @@
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
We can see the means of each month are scaled by the broadcast :
julia
julia> mean(eachslice(data; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.49795
- 2000-03-01T00:00:00  0.493708
- 2000-04-01T00:00:00  0.492377
- 2000-05-01T00:00:00  0.496201
- 2000-06-01T00:00:00  0.496665
- 2000-07-01T00:00:00  0.489762
- 2000-08-01T00:00:00  0.503719
- 2000-09-01T00:00:00  0.505946
- 2000-10-01T00:00:00  0.499719
- 2000-11-01T00:00:00  0.490968
- 2000-12-01T00:00:00  0.501068
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.499674
+ 2000-03-01T00:00:00  0.510863
+ 2000-04-01T00:00:00  0.500818
+ 2000-05-01T00:00:00  0.501124
+ 2000-06-01T00:00:00  0.495215
+ 2000-07-01T00:00:00  0.489497
+ 2000-08-01T00:00:00  0.500495
+ 2000-09-01T00:00:00  0.491479
+ 2000-10-01T00:00:00  0.499282
+ 2000-11-01T00:00:00  0.491202
+ 2000-12-01T00:00:00  0.485718
julia
julia> mean(eachslice(scaled; dims=(X, Y)))
╭────────────────────────────────╮
 │ 12-element DimArray{Float64,1} │
 ├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
- 2000-01-01T00:00:00  0.496108
- 2000-02-01T00:00:00  0.995899
- 2000-03-01T00:00:00  1.48113
- 2000-04-01T00:00:00  1.96951
- 2000-05-01T00:00:00  2.48101
- 2000-06-01T00:00:00  2.97999
- 2000-07-01T00:00:00  3.42833
- 2000-08-01T00:00:00  4.02976
- 2000-09-01T00:00:00  4.55351
- 2000-10-01T00:00:00  4.99719
- 2000-11-01T00:00:00  5.40065
- 2000-12-01T00:00:00  6.01282
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
+ 2000-01-01T00:00:00  0.50854
+ 2000-02-01T00:00:00  0.999348
+ 2000-03-01T00:00:00  1.53259
+ 2000-04-01T00:00:00  2.00327
+ 2000-05-01T00:00:00  2.50562
+ 2000-06-01T00:00:00  2.97129
+ 2000-07-01T00:00:00  3.42648
+ 2000-08-01T00:00:00  4.00396
+ 2000-09-01T00:00:00  4.42331
+ 2000-10-01T00:00:00  4.99282
+ 2000-11-01T00:00:00  5.40322
+ 2000-12-01T00:00:00  5.82862
You can also use broadcast_dims the same way:
julia
julia> broadcast_dims(*, data, month_scalars)
╭───────────────────────────────╮
 │ 100×25×12 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
@@ -114,16 +114,16 @@
   ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-   ↓ →  1          2         3         …  23          24          25
-   1    0.792885   0.896315  0.28708       0.818644    0.568131    0.967865
-   2    0.0959455  0.164724  0.491767      0.515061    0.0416986   0.31065
-   3    0.23546    0.652353  0.201734      0.568302    0.875221    0.651784
-   4    0.2344     0.390033  0.108592      0.69635     0.655684    0.564589
-   ⋮                                   ⋱                           ⋮
-  97    0.192183   0.76163   0.924417      0.0502944   0.736214    0.959028
-  98    0.204551   0.725738  0.511086      0.566557    0.268319    0.790283
-  99    0.250752   0.612779  0.974596      0.246796    0.555456    0.27181
- 100    0.295548   0.364488  0.21714   …   0.687223    0.068913    0.453973
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
+   ↓ →  1          2         3          …  23          24         25
+   1    0.624685   0.909533  0.43314        0.0541549   0.721101   0.600306
+   2    0.546357   0.621601  0.960677       0.129382    0.434374   0.438592
+   3    0.407207   0.861652  0.0837074      0.0725917   0.800084   0.749829
+   4    0.67539    0.637399  0.416444       0.853879    0.988957   0.44724
+   ⋮                                    ⋱                          ⋮
+  97    0.474087   0.475786  0.759047       0.704613    0.706165   0.1279
+  98    0.844262   0.668594  0.431398       0.746465    0.919534   0.398776
+  99    0.0471885  0.788358  0.0145691      0.647328    0.517971   0.910412
+ 100    0.0442448  0.702787  0.585658   …   0.68326     0.774131   0.403955
And with the @d macro you can set the dimension order and other properties of the output array, by passing a single assignment or a NamedTuple argument to @d after the broadcast:
julia
julia> @d data .* month_scalars dims=(Ti, X, Y)
╭───────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} │
 ├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -131,16 +131,16 @@
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415
Or
julia
julia> @d data .* month_scalars (dims=(Ti, X, Y), name=:scaled)
╭──────────────────────────────────────╮
 │ 12×100×25 DimArray{Float64,3} scaled │
 ├──────────────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00") ForwardOrdered Regular Points,
@@ -148,17 +148,17 @@
   ↗ Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
- ↓ →                   1         …  98         99         100
-  2000-01-01T00:00:00  0.792885      0.204551   0.250752    0.295548
-  2000-02-01T00:00:00  1.27802       0.728611   1.04046     1.10447
-  2000-03-01T00:00:00  1.13589       1.54853    0.686287    0.929592
-  2000-04-01T00:00:00  3.49029       1.09764    3.2115      1.38795
- ⋮                               ⋱                          ⋮
-  2000-09-01T00:00:00  1.89618       0.203703   4.47963     2.55114
-  2000-10-01T00:00:00  9.58054   …   5.3793     0.118632    7.12731
-  2000-11-01T00:00:00  0.134208      5.08512    6.19995     2.74204
-  2000-12-01T00:00:00  7.60111      11.5008    10.2026      6.54483

-    
+ ↓ →                   1         …  98         99          100
+  2000-01-01T00:00:00  0.624685      0.844262   0.0471885    0.0442448
+  2000-02-01T00:00:00  0.547848      1.79785    0.187495     0.441762
+  2000-03-01T00:00:00  2.49232       1.15489    2.82356      0.921936
+  2000-04-01T00:00:00  0.439142      2.61341    1.59542      2.24247
+ ⋮                               ⋱                           ⋮
+  2000-09-01T00:00:00  3.27042       6.61858    3.10246      5.93972
+  2000-10-01T00:00:00  0.12404   …   2.53632    7.83904      6.55713
+  2000-11-01T00:00:00  1.79388       2.5891     2.14203      5.52389
+  2000-12-01T00:00:00  8.84219       6.15004    5.04169      6.10415

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diff --git a/dev/cuda.html b/dev/cuda.html
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@@ -5,14 +5,14 @@
     
     CUDA & GPUs | DimensionalData.jl
     
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DimensionalData.jl
Appearance
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
+    
DimensionalData.jl
Appearance
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
+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 ┐
@@ -47,8 +47,8 @@
   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:
  1. Work seamlessly with Base julia broadcasts and other operations that already work on GPU.
  2. 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 converts 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 Name{:layer_name}() replaces :layer_name
  • Metadata dicts need to be stripped, 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.

-    
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}
GPU Integration goals ​
DimensionalData.jl has two GPU-related goals:
  1. Work seamlessly with Base Julia broadcasts and other operations that already work on GPU.
  2. 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.

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diff --git a/dev/dimarrays.html b/dev/dimarrays.html
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     DimArrays | DimensionalData.jl
     
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DimensionalData.jl
Appearance
DimArrays ​
DimArrays 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
+    
DimensionalData.jl
Appearance
DimArrays ​
DimArrays 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}:
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
julia
julia> da = DimArray(A, (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
julia
julia> da = DimArray(A, (X, Y))
╭──────────────────────────╮
 │ 5×10 DimArray{Float64,2} │
 ├──────────────────────────┴─────────────────────────────── dims ┐
   ↓ X, → Y
 └────────────────────────────────────────────────────────────────┘
- 0.534915   0.4518     0.194465  0.780132  …  0.476495   0.0239356  0.526295
- 0.707692   0.454886   0.588506  0.168291     0.222482   0.493417   0.134246
- 0.0999314  0.0425477  0.44119   0.410463     0.0400372  0.0622255  0.350104
- 0.132894   0.865298   0.778166  0.723438     0.0740736  0.137619   0.202743
- 0.966106   0.854076   0.895608  0.147478     0.815365   0.24737    0.860933
We can access a value with the same dimension wrappers:
julia
julia> da[Y(1), X(2)]
0.7076921858340348
There are shortcuts for creating DimArray:
julia
julia> A = rand(5, 10)
5×10 Matrix{Float64}:
- 0.764613  0.657441   0.625257   0.587556  …  0.344408  0.0140644  0.0127924
- 0.760205  0.298179   0.545129   0.989571     0.554791  0.0697249  0.949105
- 0.553558  0.500157   0.295319   0.925873     0.386433  0.392848   0.26069
- 0.912065  0.917543   0.0965083  0.997672     0.492141  0.625296   0.391155
- 0.948512  0.0621546  0.167745   0.228964     0.90251   0.710967   0.885914
julia
julia> DimArray(A, (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)]
0.2236016853688918
There are shortcuts for creating DimArray:
julia
julia> A = rand(5, 10)
5×10 Matrix{Float64}:
+ 0.670688  0.534915   0.111072  0.194465  …  0.121525  0.797168  0.0239356
+ 0.741593  0.707692   0.879899  0.813705     0.601833  0.826098  0.493417
+ 0.119505  0.0999314  0.403111  0.755958     0.289335  0.542251  0.0622255
+ 0.775377  0.217733   0.865298  0.876395     0.108514  0.306932  0.627107
+ 0.576903  0.950338   0.854076  0.511978     0.866334  0.905021  0.0238569
julia
julia> DimArray(A, (X, Y))
╭──────────────────────────╮
 │ 5×10 DimArray{Float64,2} │
 ├──────────────────────────┴─────────────────────────────── dims ┐
   ↓ X, → Y
 └────────────────────────────────────────────────────────────────┘
- 0.764613  0.657441   0.625257   0.587556  …  0.344408  0.0140644  0.0127924
- 0.760205  0.298179   0.545129   0.989571     0.554791  0.0697249  0.949105
- 0.553558  0.500157   0.295319   0.925873     0.386433  0.392848   0.26069
- 0.912065  0.917543   0.0965083  0.997672     0.492141  0.625296   0.391155
- 0.948512  0.0621546  0.167745   0.228964     0.90251   0.710967   0.885914
julia
julia> DimArray(A, (X, Y); name=:DimArray, metadata=Dict())
╭───────────────────────────────────╮
+ 0.670688  0.534915   0.111072  0.194465  …  0.121525  0.797168  0.0239356
+ 0.741593  0.707692   0.879899  0.813705     0.601833  0.826098  0.493417
+ 0.119505  0.0999314  0.403111  0.755958     0.289335  0.542251  0.0622255
+ 0.775377  0.217733   0.865298  0.876395     0.108514  0.306932  0.627107
+ 0.576903  0.950338   0.854076  0.511978     0.866334  0.905021  0.0238569
julia
julia> DimArray(A, (X, Y); name=:DimArray, metadata=Dict())
╭───────────────────────────────────╮
 │ 5×10 DimArray{Float64,2} DimArray │
 ├───────────────────────────────────┴────────────────────── dims ┐
   ↓ X, → Y
 ├────────────────────────────────────────────────────── metadata ┤
   Dict{Any, Any}()
 └────────────────────────────────────────────────────────────────┘
- 0.764613  0.657441   0.625257   0.587556  …  0.344408  0.0140644  0.0127924
- 0.760205  0.298179   0.545129   0.989571     0.554791  0.0697249  0.949105
- 0.553558  0.500157   0.295319   0.925873     0.386433  0.392848   0.26069
- 0.912065  0.917543   0.0965083  0.997672     0.492141  0.625296   0.391155
- 0.948512  0.0621546  0.167745   0.228964     0.90251   0.710967   0.885914
Constructing DimArray with arbitrary dimension names ​
For arbitrary names, we can use the Dim{:name} dims by using Symbols, and indexing with keywords:
julia
julia> da1 = DimArray(rand(5, 5), (:a, :b))
╭─────────────────────────╮
+ 0.670688  0.534915   0.111072  0.194465  …  0.121525  0.797168  0.0239356
+ 0.741593  0.707692   0.879899  0.813705     0.601833  0.826098  0.493417
+ 0.119505  0.0999314  0.403111  0.755958     0.289335  0.542251  0.0622255
+ 0.775377  0.217733   0.865298  0.876395     0.108514  0.306932  0.627107
+ 0.576903  0.950338   0.854076  0.511978     0.866334  0.905021  0.0238569
Constructing DimArray with arbitrary dimension names ​
For arbitrary names, we can use the Dim{:name} dims by using Symbols, and indexing with keywords:
julia
julia> da1 = DimArray(rand(5, 5), (:a, :b))
╭─────────────────────────╮
 │ 5×5 DimArray{Float64,2} │
 ├─────────────────────────┴──────────────────────────────── dims ┐
   ↓ a, → b
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422
and get a value, here another smaller DimArray:
julia
julia> da1[a=3, b=1:3]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.0463176
- 0.143704
- 0.965831
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)]
true
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))
╭────────────────────────────╮
+ 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)]
true
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]
- 0.0760848  0.0641884  0.680225  0.683696   0.0205228  0.563623  0.673966
- 0.0992684  0.970459   0.330777  0.241056   0.0196808  0.669021  0.107475
- 0.206406   0.444708   0.11996   0.322115   0.225865   0.802122  0.421692
- 0.94412    0.0192911  0.351413  0.468968   0.269685   0.150126  0.215458
- 0.917457   0.162553   0.184309  0.719495   0.221163   0.4231    0.721331
- 0.916702   0.794163   0.880796  0.839618   0.380161   0.180894  0.375182
- 0.48817    0.498764   0.904961  0.392377   0.625435   0.786147  0.769313
- 0.339888   0.358625   0.290734  0.0778416  0.451425   0.879774  0.605324
- 0.481184   0.828395   0.870276  0.0323182  0.195774   0.467484  0.255916
- 0.758865   0.680352   0.679221  0.920736   0.0375522  0.296639  0.139067
julia
julia> da2[(X(3), Z(5))]
╭───────────────────────────────╮
+ 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
 └─────────────────────────────────┘
- 0.794017
- 0.865233
- 0.0540645
- 0.0474302
- 0.521021
- 0.836107
- 0.167104
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}}}:
+ 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)
julia
julia> da2[inds]
╭─────────────────────────╮
@@ -112,66 +112,66 @@
   ↓ Y ,
   → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
 └──────────────────────────────────────────────────────────────────────────────┘
-  (3, 5)     (7, 4)    (8, 2)
- 0.794017   0.690695  0.89862
- 0.865233   0.992316  0.864765
- 0.0540645  0.409622  0.575698
- 0.0474302  0.133195  0.161393
- 0.521021   0.246162  0.337182
- 0.836107   0.365341  0.394703
- 0.167104   0.847018  0.213826
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)]
╭──────────────────────────╮
+  (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
 └────────────────────────────────────────────────────────────────┘
- 0.680225  0.457891   0.230047   0.634499  0.678022
- 0.330777  0.0275306  0.352138   0.739241  0.353691
- 0.11996   0.904604   0.708321   0.837335  0.0540645
- 0.351413  0.853553   0.934609   0.208365  0.803293
- 0.184309  0.91732    0.615764   0.526688  0.5573
- 0.880796  0.683091   0.761212   0.956031  0.3162
- 0.904961  0.589895   0.945371   0.409622  0.110998
- 0.290734  0.575698   0.497346   0.750906  0.354046
- 0.870276  0.320667   0.0559616  0.341835  0.234458
- 0.679221  0.950645   0.727554   0.306208  0.173513
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:
julia
julia> da1[X(3), 4]
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.
+ 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:
julia
julia> da1[X(3), 4]
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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422
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.
+ 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
 └────────────────────────────────────────────────────────────────┘
- 0.88841    0.329515   0.620055  0.20051     0.500642
- 0.284399   0.152765   0.465631  0.00337686  0.885929
- 0.0463176  0.143704   0.965831  0.754468    0.0684698
- 0.257822   0.411529   0.219583  0.6642      0.714286
- 0.706267   0.0450386  0.441735  0.137131    0.350422
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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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))
╭───────────────────────────╮
+ 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 AbstractDimArrays works with getindex, setindex! and view. The result is still an AbstracDimArray, unless using all single Int or Selectors 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]
- 0.0610019  0.578989  0.760111  0.291577
- 0.980256   0.388907  0.711714  0.827852
- 0.225583   0.395594  0.433955  0.691887
julia
julia> sum(da5; dims=Ti)
╭───────────────────────────╮
+ 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]
- 1.31572  3.28438  1.73011  2.13035
- 2.5962   1.55745  2.5074   2.07151
- 2.31468  1.40687  3.06474  3.2858
Dims keywords
Methods where dims, dim types, or Symbols 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
Performance ​
Indexing with Dimensions has no runtime cost. Let's benchmark it:
julia
julia> using BenchmarkTools
+ 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 Symbols 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
Performance ​
Indexing with Dimensions has no runtime cost. Let's benchmark it:
julia
julia> using BenchmarkTools
 
 julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
 │ 3×3 DimArray{Float64,2} │
@@ -181,25 +181,25 @@
  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):  2.785 ns … 18.735 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     2.795 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   2.822 ns ±  0.462 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 19.216 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.118 ns ±  0.327 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-                █                                            
-  ▇▃▁▁▁▁▁▁▁▁▁▁▁▁█▆▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▃▂▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
-  2.78 ns        Histogram: frequency by time        2.83 ns <
+                                █                            
+  ▃▁▆▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▆▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▃ ▂
+  3.1 ns         Histogram: frequency by time        3.12 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 … 17.072 ns  ┊ GC (min … max): 0.00% … 0.00%
- Time  (median):     3.105 ns              ┊ GC (median):    0.00%
- Time  (mean ± σ):   3.136 ns ±  0.481 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+ Range (min … max):  3.095 ns … 27.762 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.106 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.125 ns ±  0.396 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
 
-  ▆█         ▆█           ▂          ▃                       ▂
-  ██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▄▅▁▁▁▁▁▁▁▁▁▄ █
-  3.1 ns       Histogram: log(frequency) by time     3.14 ns <
+  ▇█                                                      ▁  ▁
+  ██▆▁▃▁▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▅▅▆██ █
+  3.1 ns       Histogram: log(frequency) by time     3.67 ns <
 
  Memory estimate: 0 bytes, allocs estimate: 0.

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diff --git a/dev/dimensions.html b/dev/dimensions.html
index f614bd45d..019f49c45 100644
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@@ -5,14 +5,14 @@
     
     Dimensions | DimensionalData.jl
     
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DimensionalData.jl
Appearance
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
+    
DimensionalData.jl
Appearance
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)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)
You can also make Dim dimensions with any name:
julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)
The wrapped value can be retrieved with val:
julia
julia> val(X(1))
1
DimensionalData.jl uses Dimensions everywhere:
  • Dimension are returned from dims to specify the names of the dimensions of an object
  • they wrap Lookups to associate the lookups with those names
  • to index into these objects, they wrap indices like Int or a Selector
This symmetry means we can ignore how data is organised, and label and access it by name, letting DD work out the details for us.
Dimensions are defined in the Dimensions submodule, some Dimension-specific methods can be brought into scope with:
julia
using DimensionalData.Dimensions

-    
+julia> X(1)
X 1
julia
julia> X(1), Y(2), Z(3)
(↓ X 1, → Y 2, ↗ Z 3)
You can also create Dim dimensions with any name:
julia
julia> Dim{:a}(1), Dim{:b}(1)
(↓ a 1, → b 1)
The wrapped value can be retrieved with val:
julia
julia> val(X(1))
1
DimensionalData.jl uses Dimensions everywhere:
  • Dimensions 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

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diff --git a/dev/diskarrays.html b/dev/diskarrays.html
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     DiskArrays.jl compatibility | DimensionalData.jl
     
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DimensionalData.jl
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DiskArrays.jl compatibility ​
DiskArrays.jl enables lazy, chunked application of:
  • broadcast
  • reductions
  • iteration
  • generators
  • zip
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.lj, GRIBDatasets.jl and CommonDataModel.jl
The combination of DiskArrays.jl and DimensionalData.jl is Julias answer to pythons xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.
They have no direct dependency relationships, with but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

-    
+    
DimensionalData.jl
Appearance
DiskArrays.jl compatibility ​
DiskArrays.jl enables lazy, chunked application of:
  • broadcast
  • reductions
  • iteration
  • generators
  • zip
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
The combination of DiskArrays.jl and DimensionalData.jl is Julia's answer to python's xarray. Rasters.jl and YAXArrays.jl are user-facing tools building on this combination.
They have no direct dependency relationships, but are intentionally designed to integrate via both adherence to Julia's AbstractArray interface, and by coordination during development of both packages.

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diff --git a/dev/extending_dd.html b/dev/extending_dd.html
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     Extending DimensionalData | DimensionalData.jl
     
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DimensionalData.jl
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Extending DimensionalData ​
Nearly everything in DimensionalData.jl is designed to be extensible.
  • AbstractDimArray are easily extended to custom array types. Raster or YAXArray are examples from other packages.
  • AbstractDimStack are easily extended to custom mixed array dataset. RasterStack or ArViZ.Dataset are examples.
  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.
dims, rebuild and format are the key interface methods in most of these cases.
dims ​
Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).
Dimension axes ​
Dimensions return from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.
This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.
dims keywords ​
To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.
This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.
rebuild ​
Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.
The function signature is always one of:
julia
rebuild(obj, args...)
-rebuild(obj; kw...)
rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.
These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.
If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.
An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.
For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.
format ​
When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:
julia
format(dims, array)
This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.
Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.
Interfaces.jl interface testing ​
DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.
This is the implementation definition for DimArray:
julia
julia> using DimensionalData, Interfaces
+    
DimensionalData.jl
Appearance
Extending DimensionalData ​
Nearly everything in DimensionalData.jl is designed to be extensible.
  • AbstractDimArray is easily extended to custom array types. Raster or YAXArray are examples from other packages.
  • AbstractDimStack is easily extended to custom mixed array datasets. RasterStack or ArViZ.Dataset are examples.
  • Lookup can have new types added, e.g. to AbstractSampled or AbstractCategorical. Rasters.Projected is a lookup that knows its coordinate reference system, but otherwise behaves as a regular Sampled lookup.
dims, rebuild and format are the key interface methods in most of these cases.
dims ​
Objects extending DimensionalData.jl that have dimensions must return a Tuple of constructed Dimensions from dims(obj).
Dimension axes ​
Dimensions returned from dims should hold a Lookup or in some cases just an AbstractArray (like with DimIndices). When attached to multi-dimensional objects, lookups must be the same length as the axis of the array it represents, and eachindex(A, i) and eachindex(dim) must return the same values.
This means that if the array has OffsetArrays.jl axes, the array the dimension wraps must also have OffsetArrays.jl axes.
dims keywords ​
To any dims keyword argument that usually requires the dimension I, objects should accept any Dimension, Type{<:Dimension}, Symbol, Val{:Symbol}, Val{<:Type{<:Dimension}} or also regular Integer.
This is easier than it sounds, calling DD.dims(objs, dims) will return the matching dimension and DD.dimnum(obj, dims) will return the matching Int for any of these inputs as long as dims(obj) is implemented.
rebuild ​
Rebuild methods are used to rebuild immutable objects with new field values, in a way that is more flexible and extensible than just using ConstructionBase.jl reconstruction. Developers can choose to ignore some of the fields passed by rebuild.
The function signature is always one of:
julia
rebuild(obj, args...)
+rebuild(obj; kw...)
rebuild has keyword versions automatically generated for all objects using ConstructionBase.jl.
These will work without further work as long as your object has the fields used by DimensionalData.jl objects. For example, AbstractDimArray will receive these keywords in rebuild: data, dims, refdims, name, metadata.
If your AbstractDimArray does not have all these fields, you must implement rebuild(x::YourDimArray; kw...) manually.
An argument method is also defined with the same arguments as the keyword version. For AbstractDimArray it should only be used for updating data and dims, any more that that is confusing.
For Dimension and Selector the single argument versions are easiest to use, as there is only one argument.
format ​
When constructing an AbstractDimArray or AbstractDimStack DimensionalData.format must be called on the dims tuple and the parent array:
julia
format(dims, array)
This lets DimensionalData detect the lookup properties, fill in missing fields of a Lookup, pass keywords from Dimension to detected Lookup constructors, and accept a wider range of dimension inputs like tuples of Symbol and Type.
Not calling format in the outer constructors of an AbstractDimArray has undefined behaviour.
Interfaces.jl interface testing ​
DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface and DimStackInterface.
This is the implementation definition for DimArray:
julia
julia> using DimensionalData, Interfaces
 
 julia> @implements DimensionalData.DimArrayInterface{(:refdims,:name,:metadata)} DimArray [rand(X(10), Y(10)), zeros(Z(10))]
See the DimensionalData.DimArrayInterface docs for options. We can test it with:
julia
julia> Interfaces.test(DimensionalData.DimArrayInterface)

 Testing DimArrayInterface is implemented for DimArray
@@ -50,7 +50,7 @@
 Implementation summary:
   DimArray correctly implements DimensionalData.DimArrayInterface: true
 true

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diff --git a/dev/get_info.html b/dev/get_info.html
index f1e64c5d0..16659b289 100644
--- a/dev/get_info.html
+++ b/dev/get_info.html
@@ -5,14 +5,14 @@
     
     Getters | DimensionalData.jl
     
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DimensionalData.jl
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Getters ​
DimensionalData.jl defines consistent methods to retrieve information from objects like DimArray, DimStack, Tuples of Dimension, Dimension and Lookup.
First we will define an example DimArray.
julia
using DimensionalData
+    
DimensionalData.jl
Appearance
Getters ​
DimensionalData.jl defines consistent methods to retrieve information from objects like DimArray, DimStack, Tuples 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)
╭───────────────────────────╮
@@ -29,27 +29,27 @@
   ↓ 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        …  180.0       190.0       200.0
- 10      0.664038      0.464482        0.868966    0.530653    0.418968
-  9      0.00832284    0.0980829       0.314535    0.322005    0.674881
-  8      0.639212      0.233778        0.315591    0.190225    0.475569
-  7      0.602315      0.0372362       0.950143    0.062593    0.948976
-  6      0.375166      0.423798   …    0.640311    0.932968    0.98985
-  5      0.153219      0.635963        0.293126    0.68781     0.309301
-  4      0.00508449    0.829714        0.727149    0.585756    0.921145
-  3      0.946824      0.906705        0.899632    0.471877    0.633956
-  2      0.876112      0.831617        0.128932    0.428951    0.243803
-  1      0.749253      0.864128   …    0.715102    0.921012    0.566574
dims retrieves dimensions from any object that has them.
What makes it so useful is you can filter which dimensions you want in what order, using any Dimension, Type{Dimension} or Symbol.
julia
julia> dims(A)
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered 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
dims retrieves dimensions from any object that has them.
What makes it so useful is that you can filter which dimensions you want, and specify in what order, using any Dimension, Type{Dimension} or Symbol.
julia
julia> dims(A)
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
 → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points)
julia
julia> dims(A, Y)
Y Sampled{Float64} ForwardOrdered Regular Points
 wrapping: 100.0:10.0:200.0
julia
julia> dims(A, Y())
Y Sampled{Float64} ForwardOrdered Regular Points
 wrapping: 100.0:10.0:200.0
julia
julia> dims(A, :Y)
Y Sampled{Float64} ForwardOrdered Regular Points
 wrapping: 100.0:10.0:200.0
julia
julia> dims(A, (X,))
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points)
julia
julia> dims(A, (Y, X))
(↓ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points,
 → X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points)
julia
julia> dims(A, reverse(dims(A)))
(↓ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points,
 → X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points)
julia
julia> dims(A, isregular)
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
-→ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points)
Predicates ​
These always return true or false. With multiple dimensions, fale means !all and true means all.
dims and all other methods listed above can use predicates to filter the returned dimensions.
julia
julia> issampled(A)
true
julia
julia> issampled(dims(A))
true
julia
julia> issampled(A, Y)
true
julia
julia> issampled(lookup(A, Y))
true
julia
julia> dims(A, issampled)
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
+→ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points)
Predicates ​
These always return true or false. With multiple dimensions, false means !all and true means all.
dims and all other methods listed above can use predicates to filter the returned dimensions.
julia
julia> issampled(A)
true
julia
julia> issampled(dims(A))
true
julia
julia> issampled(A, Y)
true
julia
julia> issampled(lookup(A, Y))
true
julia
julia> dims(A, issampled)
(↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
 → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points)
julia
julia> otherdims(A, issampled)
()
julia
julia> lookup(A, issampled)
Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
 Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points

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     Group By | DimensionalData.jl
     
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DimensionalData.jl
Appearance
Group By ​
DimensionalData.jl provides a groupby function for dimensional grouping. This guide will cover:
  • simple grouping with a function
  • grouping with Bins
  • grouping with another existing AbstractDimArry or Dimension
Grouping functions ​
Lets 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
+    
DimensionalData.jl
Appearance
Group By ​
DimensionalData.jl provides a groupby function for dimensional grouping. This guide covers:
  • simple grouping with a function
  • grouping with Bins
  • grouping with another existing AbstractDimArray or Dimension
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")
Lets see how some common functions work.
The hour function will transform values to hour of the day - the integers 0:23
julia
julia> hour.(tempo)
17520-element Vector{Int64}:
+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
julia
julia> hour.(tempo)
17520-element Vector{Int64}:
   0
   1
   2
@@ -43,7 +43,7 @@
  20
  21
  22
- 23
Tuple groupings
julia
julia> yearmonth.(tempo)
17520-element Vector{Tuple{Int64, Int64}}:
+ 23
Tuple groupings
julia
julia> yearmonth.(tempo)
17520-element Vector{Tuple{Int64, Int64}}:
  (2000, 1)
  (2000, 1)
  (2000, 1)
@@ -63,23 +63,23 @@
  (2001, 12)
  (2001, 12)
  (2001, 12)
- (2001, 12)
Grouping and reducing ​
Lets define an array with a time dimension of the times used above:
julia
julia> A = rand(X(1:0.01:2), Ti(tempo))
╭───────────────────────────────╮
+ (2001, 12)
Grouping and reducing ​
Let's define an array with a time dimension of the times used above:
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   0.247032              0.706417                 0.10748
- 1.01  0.749267              0.559878                 0.916216
- 1.02  0.694796              0.981865                 0.842792
- 1.03  0.214909              0.313279                 0.683712
+ 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  0.899864              0.819928                 0.313711
- 1.97  0.581748              0.0446036                0.762533
- 1.98  0.645795              0.442542                 0.522989
- 1.99  0.460633              0.346013              …  0.639499
- 2.0   0.0403793             0.561611                 0.736525
Group by month, using the month function:
julia
julia> groups = groupby(A, Ti=>month)
╭───────────────────────────────────────────────────╮
+ 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
Group by month, using the month function:
julia
julia> groups = groupby(A, Ti=>month)
╭───────────────────────────────────────────────────╮
 │ 12-element DimGroupByArray{DimArray{Float64,1},1} │
 ├───────────────────────────────────────────────────┴──────────────────── dims ┐
   ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
@@ -102,14 +102,14 @@
   Dict{Symbol, Any} with 1 entry:
   :groupby => :Ti=>month
 └──────────────────────────────────────────────────────────────────────────────┘
-  1  0.500757
-  2  0.498832
-  3  0.498388
-  4  0.500913
+  1  0.500665
+  2  0.499693
+  3  0.500331
+  4  0.499353
   ⋮
- 10  0.500277
- 11  0.498996
- 12  0.500093
Binning ​
Sometimes we want to further aggregate our groups after running a function, or just bin the raw data directly. We can use the Bins wrapper to do this.
For quick analysis, we can break our groups into N bins.
julia
julia> groupby(A, Ti=>Bins(month, 4))
╭──────────────────────────────────────────────────╮
+ 10  0.499069
+ 11  0.500155
+ 12  0.500136
Binning ​
Sometimes we want to further aggregate our groups after running a function, or just bin the raw data directly. We can use the Bins wrapper to do this.
For quick analysis, we can break our groups into N bins.
julia
julia> groupby(A, Ti=>Bins(month, 4))
╭──────────────────────────────────────────────────╮
 │ 4-element DimGroupByArray{DimArray{Float64,1},1} │
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ Ti Sampled{IntervalSets.Interval{:closed, :open, Float64}} [1.0 .. 3.75275 (closed-open), 3.75275 .. 6.5055 (closed-open), 6.5055 .. 9.25825 (closed-open), 9.25825 .. 12.011 (closed-open)] ForwardOrdered Irregular Intervals{Start}
@@ -122,7 +122,7 @@
  1.0 .. 3.75275 (closed-open)     101×4344 DimArray
  3.75275 .. 6.5055 (closed-open)  101×4368 DimArray
  6.5055 .. 9.25825 (closed-open)  101×4416 DimArray
- 9.25825 .. 12.011 (closed-open)  101×4392 DimArray
Doing this requires slightly padding the bin edges, so the lookup of the output is less than ideal.
Select by Dimension ​
We can also select by Dimensions and any objects with dims methods.
Trivially, grouping by an objects own dimension is similar to eachslice:
julia
julia> groupby(A, dims(A, Ti))
╭──────────────────────────────────────────────────────╮
+ 9.25825 .. 12.011 (closed-open)  101×4392 DimArray
Doing this requires slightly padding the bin edges, so the lookup of the output is less than ideal.
Select by Dimension ​
We can also select by Dimensions and any objects with dims methods.
Trivially, grouping by an object's own dimension is similar to eachslice:
julia
julia> groupby(A, dims(A, Ti))
╭──────────────────────────────────────────────────────╮
 │ 17520-element DimGroupByArray{DimArray{Float64,1},1} │
 ├──────────────────────────────────────────────────────┴───────────────── dims ┐
   ↓ Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Hour(1):Dates.DateTime("2001-12-30T23:00:00") ForwardOrdered Regular Points
@@ -138,7 +138,7 @@
  ⋮
  2001-12-30T22:00:00  101×1 DimArray
  2001-12-30T23:00:00  101×1 DimArray
TODO: Apply custom function (i.e. normalization) to grouped output.

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diff --git a/dev/hashmap.json b/dev/hashmap.json
index 60f6dc7ce..1a6d7021f 100644
--- a/dev/hashmap.json
+++ b/dev/hashmap.json
@@ -1 +1 @@
-{"api_dimensions.md":"SBrxkSyF","api_lookuparrays.md":"4gWyIReH","api_reference.md":"Cbzd03zL","basics.md":"1otrM5SC","broadcasts.md":"CVzEqDHf","cuda.md":"DmbWVZki","dimarrays.md":"DAoE3lud","dimensions.md":"Btc7hsPQ","diskarrays.md":"DNn9Ahrm","extending_dd.md":"DoS75dmN","get_info.md":"B7K6LY7P","groupby.md":"DESSK1W0","index.md":"uhypFJup","integrations.md":"dJo1qUjg","object_modification.md":"-cjSpi5I","plots.md":"kpY9w3i0","selectors.md":"Cu8DY1IK","stacks.md":"SmvHWrk4","tables.md":"t8vBJGld"}
+{"api_dimensions.md":"0EpvcW_-","api_lookuparrays.md":"QQHTyv_S","api_reference.md":"CbFETIiO","basics.md":"NNYczrui","broadcasts.md":"Dz88oNfL","cuda.md":"D8Q-T0cP","dimarrays.md":"BoEU5ZVn","dimensions.md":"BukE2hG-","diskarrays.md":"DGsdRCSJ","extending_dd.md":"BXSXpgVw","get_info.md":"oVG65ArA","groupby.md":"BUxX6owS","index.md":"BtPO60nF","integrations.md":"CffVJNNA","object_modification.md":"Bn8irSF4","plots.md":"D4kZ1izt","selectors.md":"BYwWS6hp","stacks.md":"DhVQ6mRN","tables.md":"C58ZkjII"}
diff --git a/dev/index.html b/dev/index.html
index 6f53a7cab..f793811b7 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -5,14 +5,14 @@
     
     DimensionalData.jl
     
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DimensionalData.jl
Appearance
DimensionalData.jl
Julia datasets with named dimensions
High performance named indexing for Julia
Getting Started
API reference
View on Github
Intelligent indexing
DimensionalData.jl provides no-cost abstractions for named indexing, and fast index lookups.
Powerful Array manipulation
broadcast, reduce, permutedims, and groupby operations.
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.

-    
+    
DimensionalData.jl
Appearance
DimensionalData.jl
Julia datasets with named dimensions
High performance named indexing for Julia
Getting Started
API reference
View on Github
Intelligent indexing
DimensionalData.jl provides no-cost abstractions for named indexing, and fast index lookups.
Powerful Array manipulation
broadcast, reduce, permutedims, and groupby operations.
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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diff --git a/dev/integrations.html b/dev/integrations.html
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     Integrations | DimensionalData.jl
     
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DimensionalData.jl
Appearance
Integrations ​
Rasters.jl ​
Rasters.jl extends DD for geospatial data manipulation, providing file load/save for a wide range of raster data sources and common GIS tools like polygon rasterization and masking. Raster types are aware of crs and their missingval (which is often not missing for performance and storage reasons).
Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.
A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSample lookups.
YAXArrays.jl ​
YAXArrays.jl is another spatial data package aimed more at (very) large datasets. It's functionality is slowly converging with Rasters.jl (both wrapping DiskArray.jl/DimensionalData.jl) and we work closely with the developers.
YAXArray is a AbstractDimArray and inherits its behaviours.
ClimateBase.jl ​
ClimateBase.jl Extends DD with methods for analysis of climate data.
ArviZ.jl ​
ArviZ.jl Is a Julia package for exploratory analysis of Bayesian models.
An ArviZ.Dataset is an AbstractDimStack!
JuMP.jl ​
JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.
CryoGrid.jl ​
CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.
CryoGridOutput uses DimArray for views into output data.
DynamicGrids.jl ​
DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.
All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!
AstroImages.jl ​
AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.
TimeseriesTools.jl ​
TimeseriesTools.jl Uses DimArray for time-series data.

-    
+    
DimensionalData.jl
Appearance
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).
Rasters.jl is also the reason DimensionalData.jl exists at all! But it always made sense to separate out spatial indexing from GIS tools and dependencies.
A Raster is a AbstractDimArray, a RasterStack is a AbstractDimStack, and Projected and Mapped are AbstractSampled lookups.
YAXArrays.jl ​
YAXArrays.jl is another spatial data package aimed more at (very) large datasets. Its functionality is slowly converging with Rasters.jl (both wrapping DiskArrays.jl/DimensionalData.jl) and we work closely with the developers.
YAXArray is a AbstractDimArray and inherits its behaviours.
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.
An ArviZ.Dataset is an AbstractDimStack!
JuMP.jl ​
JuMP.jl is a powerful optimization DSL. It defines its own named array types but now accepts any AbstractDimArray too, through a package extension.
CryoGrid.jl ​
CryoGrid.jl A Julia implementation of the CryoGrid permafrost model.
CryoGridOutput uses DimArray for views into output data.
DynamicGrids.jl ​
DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.
All DynamicGrids.jl Outputs are <: AbstractDimArray, and AbstractDimArray are used for auxiliary data to allow temporal synchronisation during simulations. Notably, this all works on GPUs!
AstroImages.jl ​
AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage is <: AbstractDimArray.
TimeseriesTools.jl ​
TimeseriesTools.jl Uses DimArray for time-series data.

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diff --git a/dev/object_modification.html b/dev/object_modification.html
index 0e49023c8..60ca958ba 100644
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+    Modifying Objects | DimensionalData.jl
     
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DimensionalData.jl
Appearance
Modifying objects ​
DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.
Everything else must be rebuilt and assigned to a variable.
modify ​
Modify the inner arrays of a AbstractDimArray or AbstractDimStack, with modify. This can be useful to e.g. replace all arrays with CuArray moving the data to the GPU, collect all inner arrays to Array without losing the outer DimArray wrappers, and similar things.
julia
julia> using DimensionalData
+    
DimensionalData.jl
Appearance
Modifying Objects ​
DimensionalData.jl objects are all struct rather than mutable struct. The only things you can modify in-place are the values of the contained arrays or metadata Dicts if they exist.
Everything else must be rebuilt and assigned to a variable.
modify ​
Modify the inner arrays of a AbstractDimArray or AbstractDimStack, with modify. This can be useful to e.g. replace all arrays with CuArray moving the data to the GPU, collect all inner arrays to Array without losing the outer DimArray wrappers, and similar things.
julia
julia> using DimensionalData
 
 julia> A = falses(X(3), Y(5))
╭──────────────────────╮
 │ 3×5 DimArray{Bool,2} │
@@ -53,28 +53,28 @@
   → Y Categorical{Char} 'a':1:'n' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'b'        'c'      …   'l'       'm'       'n'
- 3.0  0.338844  0.461631   0.207882     0.804846  0.69461   0.340557
- 2.0  0.668984  0.803446   0.406397     0.138819  0.21316   0.907202
- 1.0  0.450109  0.0702987  0.28035      0.183103  0.770553  0.352592
mergedims ​
mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.
rebuild ​
rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.
Warning
rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.
julia
julia> A1 = rebuild(A; name=:my_array)
╭───────────────────────────────────╮
+ 3.0  0.884218  0.686915   0.208271     0.744517  0.487197  0.86789
+ 2.0  0.903705  0.0915867  0.247032     0.824556  0.298728  0.933212
+ 1.0  0.159092  0.193627   0.317813     0.474755  0.474052  0.364844
mergedims ​
mergedims is like reshape, but simultaneously merges multiple dimensions into a single combined dimension with a lookup holding Tuples of the values of both dimensions.
rebuild ​
rebuild is one of the core functions of DimensionalData.jl. Basically everything uses it somewhere. And you can too, with a few caveats.
Warning
rebuild assumes you know what you are doing. You can quite easily set values to things that don't make sense. The constructor may check a few things, like the number of dimensions matches the axes of the array. But not much else.
julia
julia> A1 = rebuild(A; name=:my_array)
╭───────────────────────────────────╮
 │ 3×14 DimArray{Float64,2} my_array │
 ├───────────────────────────────────┴──────────────────────────────────── dims ┐
   ↓ X Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
   → Y Categorical{Char} 'a':1:'n' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'b'        'c'      …   'l'       'm'       'n'
- 1.0  0.450109  0.0702987  0.28035      0.183103  0.770553  0.352592
- 2.0  0.668984  0.803446   0.406397     0.138819  0.21316   0.907202
- 3.0  0.338844  0.461631   0.207882     0.804846  0.69461   0.340557
julia
julia> name(A1)
:my_array
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> d = X(1)
X 1
julia
julia> rebuild(d, 1:10)
X 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.
TypeKeywordsArguments
AbstractDimArraydata, dims, [refdims, name, metadata]as with kw, in order
AbstractDimStackdata, dims, [refdims], layerdims, [metadata, layermetadata]as with kw, in order
Dimensionvalval
Selectorval, (atol)val
Lookupdata, (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)
(a = 1, b = 2)
julia
julia> rebuild(nt, a = 99)
(a = 99, b = 2)
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.
julia
julia> set(A, Y => Z)
╭──────────────────────────╮
+ 1.0  0.159092  0.193627   0.317813     0.474755  0.474052  0.364844
+ 2.0  0.903705  0.0915867  0.247032     0.824556  0.298728  0.933212
+ 3.0  0.884218  0.686915   0.208271     0.744517  0.487197  0.86789
julia
julia> name(A1)
:my_array
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> d = X(1)
X 1
julia
julia> rebuild(d, 1:10)
X 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.
TypeKeywordsArguments
AbstractDimArraydata, dims, [refdims, name, metadata]as with kw, in order
AbstractDimStackdata, dims, [refdims], layerdims, [metadata, layermetadata]as with kw, in order
Dimensionvalval
Selectorval, (atol)val
Lookupdata, (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)
(a = 1, b = 2)
julia
julia> rebuild(nt, a = 99)
(a = 99, b = 2)
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.
julia
julia> set(A, Y => Z)
╭──────────────────────────╮
 │ 3×14 DimArray{Float64,2} │
 ├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ X Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
   → Z Categorical{Char} 'a':1:'n' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   'a'       'b'        'c'      …   'l'       'm'       'n'
- 1.0  0.450109  0.0702987  0.28035      0.183103  0.770553  0.352592
- 2.0  0.668984  0.803446   0.406397     0.138819  0.21316   0.907202
- 3.0  0.338844  0.461631   0.207882     0.804846  0.69461   0.340557

-    
+ 1.0  0.159092  0.193627   0.317813     0.474755  0.474052  0.364844
+ 2.0  0.903705  0.0915867  0.247032     0.824556  0.298728  0.933212
+ 3.0  0.884218  0.686915   0.208271     0.744517  0.487197  0.86789

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diff --git a/dev/plots.html b/dev/plots.html
index 8f9740b59..4bccf983a 100644
--- a/dev/plots.html
+++ b/dev/plots.html
@@ -5,14 +5,14 @@
     
     Plots.jl | DimensionalData.jl
     
-    
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@@ -20,12 +20,12 @@
     
   
   
-    
DimensionalData.jl
Appearance
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
+    
DimensionalData.jl
Appearance
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:
julia
Makie.rainclouds(A)
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!

-    
+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:
julia
Makie.rainclouds(A)
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!

+    
     
   
 
\ No newline at end of file
diff --git a/dev/selectors.html b/dev/selectors.html
index 75b26057f..d8c2540e0 100644
--- a/dev/selectors.html
+++ b/dev/selectors.html
@@ -5,14 +5,14 @@
     
     Selectors | DimensionalData.jl
     
-    
-    
+    
+    
     
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+    
     
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-    
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+    
+    
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@@ -20,37 +20,37 @@
     
   
   
-    
DimensionalData.jl
Appearance
Selectors ​
As well as choosing dimensions by name, we can also select values in them.
First, we can create 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]))
╭─────────────────────────╮
+    
DimensionalData.jl
Appearance
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  0.772277  0.536911   0.101231
- 1.2  0.711133  0.0653496  0.339858
- 1.4  0.883222  0.748041   0.191494
- 1.6  0.802776  0.621603   0.341976
- 1.8  0.156538  0.768488   0.87255
- 2.0  0.969079  0.869012   0.415714
Then we can use Selector to select values from the array:
At(x) gets the index or indices exactly matching the passed in value/s.
julia
julia> A[X=At(1.2), Y=At(:c)]
0.33985840915074383
Or within a tolerance:
julia
julia> A[X=At(0.99:0.201:1.5; atol=0.05)]
╭─────────────────────────╮
+ ↓ →   :a        :b        :c
+ 1.0  0.782081  0.381692  0.751977
+ 1.2  0.836407  0.992013  0.806006
+ 1.4  0.429267  0.367576  0.376108
+ 1.6  0.127082  0.114532  0.0943594
+ 1.8  0.677743  0.354013  0.910098
+ 2.0  0.822783  0.874735  0.144269
Then we can use the Selector to select values from the array:
The At(x) selector gets the index or indices exactly matching the passed in value(s).
julia
julia> A[X=At(1.2), Y=At(:c)]
0.806005979085237
Or within a tolerance:
julia
julia> A[X=At(0.99:0.201:1.5; atol=0.05)]
╭─────────────────────────╮
 │ 3×3 DimArray{Float64,2} │
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
   ↓ X Sampled{Float64} [1.0, 1.2, 1.4] ForwardOrdered Irregular Points,
   → Y Categorical{Symbol} [:a, :b, :c] ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
- ↓ →   :a        :b         :c
- 1.0  0.772277  0.536911   0.101231
- 1.2  0.711133  0.0653496  0.339858
- 1.4  0.883222  0.748041   0.191494
At can also take vectors and ranges:
julia
julia> A[X=At(1.2:0.2:1.5), Y=At([:a, :c])]
╭─────────────────────────╮
+ ↓ →   :a        :b        :c
+ 1.0  0.782081  0.381692  0.751977
+ 1.2  0.836407  0.992013  0.806006
+ 1.4  0.429267  0.367576  0.376108
At can also take vectors and ranges:
julia
julia> A[X=At(1.2:0.2:1.5), Y=At([:a, :c])]
╭─────────────────────────╮
 │ 2×2 DimArray{Float64,2} │
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
   ↓ X Sampled{Float64} [1.2, 1.4] ForwardOrdered Irregular Points,
   → Y Categorical{Symbol} [:a, :c] ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
  ↓ →   :a        :c
- 1.2  0.711133  0.339858
- 1.4  0.883222  0.191494
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
Sampled(x) lookups hold values sampled along an axis. They may be Ordered/Unordered, Intervals/Points, and Regular/Irregular.
Most of these properties are usually detected automatically, but here we create a Sampled lookup manually:
julia
julia> l = Sampled(10.0:10.0:100.0; order=ForwardOrdered(), span=Regular(10.0), sampling=Intervals(Start()))
Sampled{Float64} ForwardOrdered Regular Intervals{Start}
-wrapping: 10.0:10.0:100.0
To specify Irregular Intervals we should include the outer bounds of the lookup, as we cant determine them from the vector.
julia
julia> l = Sampled([13, 8, 5, 3, 2, 1]; order=ForwardOrdered(), span=Irregular(1, 21), sampling=Intervals(Start()))
Sampled{Int64} ForwardOrdered Irregular Intervals{Start}
+ 1.2  0.836407  0.806006
+ 1.4  0.429267  0.376108
Lookups ​
Selectors find indices in the Lookup of each dimension. Lookups wrap other AbstractArray (often AbstractRange) but add additional traits to facilitate fast lookups or specifying point or interval behaviour. These are usually detected automatically.
julia
using DimensionalData.Lookups
The Sampled(x) lookup holds values sampled along an axis. They may be Ordered/Unordered, Intervals/Points, and Regular/Irregular.
Most of these properties are usually detected automatically, but here we create a Sampled lookup manually:
julia
julia> l = Sampled(10.0:10.0:100.0; order=ForwardOrdered(), span=Regular(10.0), sampling=Intervals(Start()))
Sampled{Float64} ForwardOrdered Regular Intervals{Start}
+wrapping: 10.0:10.0:100.0
To specify Irregular Intervals, we should include the outer bounds of the lookup, as we can't determine them from the vector.
julia
julia> l = Sampled([13, 8, 5, 3, 2, 1]; order=ForwardOrdered(), span=Irregular(1, 21), sampling=Intervals(Start()))
Sampled{Int64} ForwardOrdered Irregular Intervals{Start}
 wrapping: 6-element Vector{Int64}:
  13
   8
@@ -64,82 +64,82 @@
   → Y Categorical{Symbol} [:a, :b, :c, :d, :e] ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
   ↓ →   :a        :b        :c        :d        :e
- 10    0.441181  0.12452   0.201129  0.12668   0.647225
- 20    0.621662  0.196478  0.792553  0.575595  0.357367
- 30    0.72217   0.791844  0.883323  0.915251  0.246886
- 40    0.896257  0.758149  0.679453  0.506221  0.667841
- 50    0.301659  0.229418  0.442111  0.680987  0.0429074
- 60    0.973622  0.228248  0.882656  0.396585  0.870348
- 70    0.942925  0.749731  0.683795  0.687921  0.825204
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 Selectors At, Near or Contains.
julia
julia> A = rand(X(1.0:0.2:2.0), Y(10:2:20))
╭─────────────────────────╮
+ 10    0.659048  0.883222  0.621603  0.41767   0.48849
+ 20    0.796999  0.872742  0.768488  0.594101  0.956886
+ 30    0.27736   0.530405  0.721125  0.530389  0.00114293
+ 40    0.444305  0.226064  0.806166  0.794725  0.522816
+ 50    0.184738  0.764895  0.496058  0.298029  0.305342
+ 60    0.772277  0.86273   0.973357  0.491103  0.927836
+ 70    0.711133  0.748041  0.925367  0.976465  0.239921
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   0.11787     0.664642   0.0831822   0.528009   0.631878   0.990294
- 1.2   0.0905873   0.30391    0.552153    0.229792   0.29205    0.610173
- 1.4   0.495624    0.810377   0.578253    0.574678   0.371583   0.460775
- 1.6   0.263531    0.361536   0.753597    0.568763   0.564657   0.788252
- 1.8   0.388768    0.44818    0.0300922   0.896624   0.952489   0.216905
- 2.0   0.422318    0.499307   0.228081    0.823123   0.10454    0.931705
We can define another array with partly matching indices
julia
julia> B = rand(X(1.0:0.04:2.0), Y(20:-1:10))
╭───────────────────────────╮
+ ↓ →  10          12           14         16         18         20
+ 1.0   0.672504    0.72217      0.196478   0.650675   0.238831   0.396585
+ 1.2   0.71372     0.896257     0.791844   0.97293    0.570603   0.687921
+ 1.4   0.905616    0.00498986   0.758149   0.883323   0.929034   0.880178
+ 1.6   0.0928922   0.307762     0.229418   0.679453   0.21921    0.855976
+ 1.8   0.441181    0.651538     0.75985    0.442111   0.506221   0.0575993
+ 2.0   0.621662    0.314906     0.152068   0.882656   0.680987   0.771237
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         18         …  12          11         10
- 1.0    0.300304    0.660657   0.677543      0.712342    0.806495   0.727464
- 1.04   0.421525    0.835674   0.458427      0.248873    0.412078   0.455255
- 1.08   0.399781    0.824659   0.392334      0.675459    0.95213    0.91666
- 1.12   0.717006    0.623124   0.482654      0.964964    0.304266   0.313361
+ 1.0    0.0775482   0.473944   0.898833      0.746037    0.594523   0.312277
+ 1.04   0.838572    0.631878   0.429465      0.812342    0.737151   0.5053
+ 1.08   0.166221    0.29205    0.244582      0.363678    0.801242   0.328169
+ 1.12   0.11787     0.371583   0.400001      0.92906     0.337296   0.760043
  ⋮                                       ⋱               ⋮
- 1.84   0.880853    0.578329   0.394576      0.255579    0.750947   0.27206
- 1.88   0.363918    0.533041   0.545522      0.516268    0.531012   0.503183
- 1.92   0.259597    0.358535   0.748884      0.575727    0.227865   0.865713
- 1.96   0.34589     0.94196    0.657054  …   0.728852    0.462859   0.767151
- 2.0    0.0143204   0.985407   0.476387      0.0417586   0.286      0.777391
And we can simply select values from B with selectors from A:
julia
julia> B[DimSelectors(A)]
╭─────────────────────────╮
+ 1.84   0.528009    0.951796   0.188707      0.0411113   0.608263   0.993001
+ 1.88   0.229792    0.565165   0.910836      0.162784    0.410125   0.699118
+ 1.92   0.574678    0.618541   0.187648      0.952744    0.583476   0.460775
+ 1.96   0.89559     0.812106   0.953145  …   0.727405    0.559465   0.788252
+ 2.0    0.595409    0.630782   0.298791      0.391258    0.320737   0.216905
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   0.727464   0.712342    0.14203    0.207735    0.677543   0.300304
- 1.2   0.815253   0.642187    0.49973    0.0387029   0.319496   0.887979
- 1.4   0.391307   0.197423    0.166425   0.48124     0.300285   0.0574938
- 1.6   0.49224    0.795274    0.24625    0.735391    0.699976   0.973586
- 1.8   0.539197   0.0661958   0.811917   0.822008    0.848624   0.930699
- 2.0   0.777391   0.0417586   0.873218   0.834418    0.476387   0.0143204
If the lookups aren't aligned we can use Near instead of At, which like doing a nearest neighbor interpolation:
julia
julia> C = rand(X(1.0:0.007:2.0), Y(10.0:0.9:30))
╭────────────────────────────╮
+ ↓ →  10         12         14          16          18          20
+ 1.0   0.312277   0.746037   0.0623229   0.930614    0.898833    0.0775482
+ 1.2   0.825676   0.208304   0.232648    0.856029    0.649595    0.495624
+ 1.4   0.87475    0.403556   0.0990865   0.571126    0.0564029   0.909826
+ 1.6   0.939546   0.097066   0.139173    0.0566306   0.833475    0.0831822
+ 1.8   0.747695   0.721253   0.998253    0.781923    0.554833    0.228081
+ 2.0   0.216905   0.391258   0.719849    0.127621    0.298791    0.595409
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     0.168617     0.417756       0.258764    0.195897    0.747615
- 1.007   0.395053     0.994295       0.770101    0.403836    0.891596
- 1.014   0.144422     0.370454       0.878565    0.753744    0.0149561
- 1.021   0.938771     0.917143       0.679296    0.180397    0.436168
- ⋮                               ⋱
- 1.966   0.454047     0.92977        0.662833    0.160403    0.276742
- 1.973   0.846736     0.0923954  …   0.43915     0.405912    0.245638
- 1.98    0.946655     0.0506805      0.0821229   0.0787968   0.613895
- 1.987   0.00686195   0.266013       0.485952    0.193179    0.0628947
- 1.994   0.84475      0.574619       0.0760101   0.950007    0.662413
julia
julia> C[DimSelectors(A; selectors=Near)]
╭─────────────────────────╮
+ ↓ →    10.0       10.9       11.8        …  28.0       28.9       29.8
+ 1.0     0.412631   0.707629   0.640577       0.329029   0.945549   0.505129
+ 1.007   0.300304   0.66424    0.948532       0.565649   0.27236    0.782344
+ 1.014   0.421525   0.564636   0.267693       0.861783   0.53422    0.906871
+ 1.021   0.399781   0.381315   0.970303       0.449093   0.560553   0.565202
+ ⋮                                        ⋱
+ 1.966   0.608086   0.605375   0.336929       0.891637   0.537974   0.509288
+ 1.973   0.500679   0.179953   0.183503   …   0.851044   0.206886   0.824656
+ 1.98    0.673494   0.613381   0.0136364      0.427523   0.397621   0.742451
+ 1.987   0.294489   0.949143   0.520037       0.307031   0.18378    0.531598
+ 1.994   0.725774   0.525431   0.870641       0.919448   0.09814    0.331422
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     0.168617    0.397386    0.133475    0.0630398   0.247976   0.145293
- 1.203   0.233685    0.243195    0.331147    0.584048    0.717841   0.832538
- 1.399   0.0164892   0.729706    0.0315018   0.899356    0.847872   0.358513
- 1.602   0.0426808   0.61781     0.808889    0.140817    0.188976   0.054083
- 1.798   0.226699    0.0983553   0.661444    0.750622    0.650842   0.986606
- 1.994   0.84475     0.366873    0.734751    0.590953    0.621527   0.23502

-    
+ ↓ →    10.0        11.8       13.6       16.3        18.1       19.9
+ 1.0     0.412631    0.640577   0.505726   0.371838    0.49487    0.0639461
+ 1.203   0.824659    0.913321   0.510752   0.29488     0.154653   0.647062
+ 1.399   0.366634    0.653364   0.568727   0.925888    0.660823   0.645043
+ 1.602   0.0515532   0.324073   0.424831   0.808144    0.838211   0.71953
+ 1.798   0.205861    0.414017   0.453507   0.526825    0.180915   0.125626
+ 1.994   0.725774    0.870641   0.484227   0.0294193   0.6675     0.712805

+    
     
   
 
\ No newline at end of file
diff --git a/dev/stacks.html b/dev/stacks.html
index c38488a0e..7cc96a180 100644
--- a/dev/stacks.html
+++ b/dev/stacks.html
@@ -5,14 +5,14 @@
     
     DimStacks | DimensionalData.jl
     
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+    
+    
     
     
     
@@ -20,7 +20,7 @@
     
   
   
-    
DimensionalData.jl
Appearance
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
+    
DimensionalData.jl
Appearance
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)))
╭───────────────╮
@@ -33,33 +33,33 @@
   :b eltype: Float64 dims: X, Y size: 10×6
   :c eltype: Float64 dims: Y size: 6
   :d eltype: Float64 dims: X size: 10
-└──────────────────────────────────────────────────────────────────────────────┘
The behaviour of a DimStack is at times like a NamedTuple of DimArray and, others an AbstractArray of NamedTuple.
NamedTuple-like indexing ​
Layers can be accessed with .name or [:name]
julia
julia> st.a
╭────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
The behavior of a DimStack is at times like a NamedTuple of DimArray and, at other times, an AbstractArray of NamedTuple.
NamedTuple-like indexing ​
Layers can be accessed with .name or [:name]
julia
julia> st.a
╭────────────────────────────╮
 │ 10×6 DimArray{Float64,2} a │
 ├────────────────────────────┴─────────────────────────────────────────── 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
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  5.0       6.0       7.0        8.0        9.0        10.0
-  1.0  0.111155  0.87656   0.0963009  0.257129   0.11311     0.781497
-  2.0  0.386306  0.668226  0.278077   0.52735    0.775634    0.440884
-  3.0  0.348826  0.691687  0.964227   0.76448    0.0852624   0.52036
-  4.0  0.520184  0.484929  0.013144   0.269905   0.204084    0.117737
-  5.0  0.451525  0.247586  0.602631   0.793532   0.842029    0.0683416
-  6.0  0.669966  0.640974  0.0171909  0.749553   0.433769    0.452694
-  7.0  0.969881  0.130252  0.369225   0.518483   0.0141696   0.714084
-  8.0  0.517605  0.93358   0.515445   0.0758114  0.193109    0.150559
-  9.0  0.072176  0.474535  0.778122   0.0768485  0.80023     0.91305
- 10.0  0.854239  0.735973  0.0102776  0.0299112  0.537046    0.938732
julia
julia> st[:c]
╭─────────────────────────────────╮
+  ↓ →  5.0        6.0        7.0        8.0        9.0       10.0
+  1.0  0.105474   0.924846   0.935937   0.956044   0.841169   0.178846
+  2.0  0.606746   0.31628    0.745734   0.912254   0.581458   0.287284
+  3.0  0.0461633  0.0979352  0.497456   0.940199   0.459336   0.477439
+  4.0  0.341785   0.25853    0.788056   0.392071   0.277507   0.048092
+  5.0  0.275784   0.130942   0.0251457  0.0883392  0.257433   0.0878344
+  6.0  0.659836   0.220699   0.249439   0.0571503  0.192702   0.620657
+  7.0  0.168617   0.842552   0.935022   0.544803   0.970824   0.704105
+  8.0  0.395053   0.565883   0.19689    0.569194   0.317199   0.574623
+  9.0  0.790418   0.0329394  0.389598   0.879411   0.780527   0.579492
+ 10.0  0.113361   0.106544   0.8698     0.722335   0.544125   0.562491
julia
julia> st[:c]
╭─────────────────────────────────╮
 │ 6-element DimArray{Float64,1} c │
 ├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
-  5.0  0.0406084
-  6.0  0.01576
-  7.0  0.104261
-  8.0  0.12714
-  9.0  0.370194
- 10.0  0.48022
Array-like indexing ​
Indexing with a scalar returns a NamedTuple of values, one for each layer:
julia
julia> st[X=1, Y=4]
(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
julia
julia> maximum(st)
(a = 0.9698812177371097, b = 0.9982064541308482, c = 0.4802202427553709, d = 0.9919365440188083)
julia
julia> maximum(st; dims=Y)
╭───────────────╮
+  5.0  0.150559
+  6.0  0.91305
+  7.0  0.938732
+  8.0  0.498429
+  9.0  0.887397
+ 10.0  0.576224
Array-like indexing ​
Indexing with a scalar returns a NamedTuple of values, one for each layer:
julia
julia> st[X=1, Y=4]
(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
Reducing functions ​
Base functions like mean, maximum, reverse are applied to all layers of the stack.
julia
using Statistics
julia
julia> maximum(st)
(a = 0.9708243398463525, b = 0.97164033242648, c = 0.938732432349465, d = 0.8030281996030815)
julia
julia> maximum(st; dims=Y)
╭───────────────╮
 │ 10×1 DimStack │
 ├───────────────┴──────────────────────────────────────────────────────── dims ┐
   ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
@@ -69,7 +69,7 @@
   :b eltype: Float64 dims: X, Y size: 10×1
   :c eltype: Float64 dims: Y size: 1
   :d eltype: Float64 dims: X size: 10
-└──────────────────────────────────────────────────────────────────────────────┘
broadcast_dims broadcasts functions over any mix of AbstractDimStack and AbstractDimArray returning a new AbstractDimStack with layers the size of the largest layer in the broadcast. This will work even if dimension permutation does not match in the objects.
Only matrix layers can be rotated
julia
julia> rotl90(st[(:a, :b)])
╭───────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
broadcast_dims broadcasts functions over any mix of AbstractDimStack and AbstractDimArray returning a new AbstractDimStack with layers the size of the largest layer in the broadcast. This will work even if dimension permutation does not match in the objects.
Only matrix layers can be rotated
julia
julia> rotl90(st[(:a, :b)])
╭───────────────╮
 │ 6×10 DimStack │
 ├───────────────┴──────────────────────────────────────────────────────── dims ┐
   ↓ Y Sampled{Float64} 10.0:-1.0:5.0 ReverseOrdered Regular Points,
@@ -85,12 +85,12 @@
 ├────────────────────────────────────────────────────────────────────── layers ┤
   :a eltype: Float64 dims: X, Y size: 10×6
   :b eltype: Float64 dims: X, Y size: 10×6
-└──────────────────────────────────────────────────────────────────────────────┘
Performance ​
Indexing stack is fast - indexing a single value return a NamedTuple from all layers is usually measures 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
+└──────────────────────────────────────────────────────────────────────────────┘
Performance ​
Indexing a stack is fast - indexing a single value and returning a NamedTuple 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]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.028 ns (0 allocations: 0 bytes)
-(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)

-    
+julia> @btime $st[X=1, Y=4]
  4.067 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)
julia
julia> @btime $st[1, 4]
  4.038 ns (0 allocations: 0 bytes)
+(a = 0.9560436529181846, b = 0.5154448003579335, c = 0.49842910686075803, d = 0.48240020633837055)

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tables.html b/dev/tables.html
index dbc8cbeab..274f14d35 100644
--- a/dev/tables.html
+++ b/dev/tables.html
@@ -5,14 +5,14 @@
     
     Tables and DataFrames | DimensionalData.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -20,11 +20,11 @@
     
   
   
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DimensionalData.jl
Appearance
Tables and DataFrames ​
Tables.jl provides an ecosystem-wide interface to tabular data in Julia, giving 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 or the array or the stack layer key. Dimension columns use the Symbol version (the result of DD.name(dimension)).
Looping of dimensions and stack layers is done lazily, and does not allocate unless collected.
Example ​
julia
using DimensionalData
+    
DimensionalData.jl
Appearance
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')
julia
julia> A = rand(x, y, c; name=:data)
╭───────────────────────────────────╮
+↗ category 'a':1:'z')
julia
julia> A = rand(x, y, c; name=:data)
╭───────────────────────────────────╮
 │ 10×10×26 DimArray{Float64,3} data │
 ├───────────────────────────────────┴──────────────────────────────────── dims ┐
   ↓ X        Sampled{Int64} 1:10 ForwardOrdered Regular Points,
@@ -32,59 +32,59 @@
   ↗ category Categorical{Char} 'a':1:'z' ForwardOrdered
 └──────────────────────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  1         2         3           …  8          9         10
-  1    0.921958  0.128827  0.517175       0.947312   0.743407   0.0120967
-  2    0.449491  0.176024  0.914911       0.674061   0.291555   0.266342
-  3    0.581131  0.436605  0.694066       0.0325131  0.645678   0.620473
-  4    0.194849  0.580091  0.176766       0.888721   0.890574   0.985377
-  ⋮                                    ⋱                        ⋮
-  7    0.191934  0.359421  0.51621        0.828161   0.516313   0.74172
-  8    0.450701  0.341133  0.00110588     0.190482   0.245671   0.819605
-  9    0.336376  0.636476  0.45655        0.16484    0.294486   0.457841
- 10    0.963657  0.353968  0.199484    …  0.104763   0.887294   0.670607
Converting to DataFrame ​
Arrays will have columns for each dimension, and only one data column
julia
julia> DataFrame(A)
2600×4 DataFrame
+  ↓ →  1          2         3         …  8         9         10
+  1    0.846254   0.684303  0.939201     0.61378   0.231047   0.0950183
+  2    0.782777   0.224351  0.240351     0.319803  0.554735   0.99531
+  3    0.807522   0.767136  0.390515     0.207789  0.204906   0.605097
+  4    0.960754   0.73427   0.71403      0.317665  0.685225   0.66882
+  ⋮                                   ⋱                       ⋮
+  7    0.685603   0.444969  0.899242     0.374729  0.778193   0.556711
+  8    0.0987646  0.628875  0.901156     0.571628  0.652397   0.057459
+  9    0.596036   0.110489  0.215681     0.998548  0.918952   0.806689
+ 10    0.603358   0.245564  0.431383  …  0.258165  0.233468   0.99726
Converting to DataFrame ​
Arrays will have columns for each dimension, and only one data column
julia
julia> DataFrame(A)
2600×4 DataFrame
   Row │ X      Y      category  data
       │ Int64  Int64  Char      Float64
-──────┼──────────────────────────────────
-    1 │     1      1  a         0.921958
-    2 │     2      1  a         0.449491
-    3 │     3      1  a         0.581131
-    4 │     4      1  a         0.194849
-    5 │     5      1  a         0.364097
-    6 │     6      1  a         0.022593
-    7 │     7      1  a         0.191934
-    8 │     8      1  a         0.450701
-  ⋮   │   ⋮      ⋮       ⋮         ⋮
- 2594 │     4     10  z         0.236866
- 2595 │     5     10  z         0.894053
- 2596 │     6     10  z         0.350024
- 2597 │     7     10  z         0.417756
- 2598 │     8     10  z         0.125477
- 2599 │     9     10  z         0.599789
- 2600 │    10     10  z         0.363373
-                        2585 rows omitted
Converting to CSV ​
We can also write arrays and stacks directly to CSV.jl, or any other data type supporting the Tables.jl interface.
julia
using CSV
+──────┼───────────────────────────────────
+    1 │     1      1  a         0.846254
+    2 │     2      1  a         0.782777
+    3 │     3      1  a         0.807522
+    4 │     4      1  a         0.960754
+    5 │     5      1  a         0.0965086
+    6 │     6      1  a         0.889194
+    7 │     7      1  a         0.685603
+    8 │     8      1  a         0.0987646
+  ⋮   │   ⋮      ⋮       ⋮          ⋮
+ 2594 │     4     10  z         0.404604
+ 2595 │     5     10  z         0.0769145
+ 2596 │     6     10  z         0.292495
+ 2597 │     7     10  z         0.227142
+ 2598 │     8     10  z         0.635786
+ 2599 │     9     10  z         0.210417
+ 2600 │    10     10  z         0.849817
+                         2585 rows omitted
Converting to CSV ​
We can also write arrays and stacks directly to CSV.jl, or any other data type supporting the Tables.jl interface.
julia
using CSV
 CSV.write("dimstack.csv", st)
 readlines("dimstack.csv")
2601-element Vector{String}:
  "X,Y,category,data1,data2"
- "1,1,a,0.5501481631111826,0.9447511416331498"
- "2,1,a,0.09300753748828394,0.15945803739833375"
- "3,1,a,0.48952511607945026,0.6146564273146751"
- "4,1,a,0.7938317326707394,0.9770663775826343"
- "5,1,a,0.0019198597596568057,0.798655984630017"
- "6,1,a,0.8612776980335002,0.6730273352488979"
- "7,1,a,0.20758428874582302,0.7683418213117802"
- "8,1,a,0.023646798570656102,0.012300334322905893"
- "9,1,a,0.11925244363082943,0.4278264986513013"
+ "1,1,a,0.4473266106898912,0.55560637324799"
+ "2,1,a,0.9655293490481315,0.10276733254788795"
+ "3,1,a,0.48748811097285394,0.22237128922242078"
+ "4,1,a,0.2674330482715843,0.5501481631111826"
+ "5,1,a,0.5992407552660244,0.09300753748828394"
+ "6,1,a,0.19219227965820063,0.48952511607945026"
+ "7,1,a,0.6072910004472037,0.7938317326707394"
+ "8,1,a,0.9219584479428687,0.0019198597596568057"
+ "9,1,a,0.9217489713778885,0.44833963865079907"
  ⋮
- "2,10,z,0.8146121812750928,0.3434655288098666"
- "3,10,z,0.38167574879167476,0.24524306337289326"
- "4,10,z,0.8719143923648308,0.1985699519321249"
- "5,10,z,0.5040228055200978,0.694278906020718"
- "6,10,z,0.35100608350331053,0.20636222545147498"
- "7,10,z,0.8560905731682101,0.8428656510212863"
- "8,10,z,0.04599641808658339,0.6150940338022266"
- "9,10,z,0.005163666306917225,0.768924169642427"
- "10,10,z,0.8288995877624121,0.8136963000324107"

-    
+ "2,10,z,0.39189449474581917,0.42429962356614626"
+ "3,10,z,0.21066413424755615,0.836907965319872"
+ "4,10,z,0.10723267004015569,0.9630127530566065"
+ "5,10,z,0.9341886269251364,0.8219403925584018"
+ "6,10,z,0.29448593792551514,0.36851882799081104"
+ "7,10,z,0.6163310918833074,0.23350386812772128"
+ "8,10,z,0.020158765333216477,0.7959265671836858"
+ "9,10,z,0.3343926384599254,0.3777991041100621"
+ "10,10,z,0.4858762080349691,0.2276004407628871"

+    
     
   
 
\ No newline at end of file