diff --git a/previews/PR801/404.html b/previews/PR801/404.html new file mode 100644 index 000000000..319d9f47a --- /dev/null +++ b/previews/PR801/404.html @@ -0,0 +1,22 @@ + + + + + + 404 | DimensionalData.jl + + + + + + + + + + + +
+ + + + \ No newline at end of file diff --git a/previews/PR801/api/dimensions.html b/previews/PR801/api/dimensions.html new file mode 100644 index 000000000..f8daa8382 --- /dev/null +++ b/previews/PR801/api/dimensions.html @@ -0,0 +1,181 @@ + + + + + + Dimensions | DimensionalData.jl + + + + + + + + + + + + + + +
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'])
+ti = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
+
+A = DimArray(zeros(3, 5, 12), (y, x, ti))
+
+# output
+
+╭────────────────────────────╮
+│ 3×5×12 DimArray{Float64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Y  Categorical{Char} ['a', 'b', 'c'] ForwardOrdered,
+  → X  Sampled{Int64} 2:2:10 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+[:, :, 1]
+ ↓ →   2    4    6    8    10
+  'a'  0.0  0.0  0.0  0.0   0.0
+  'b'  0.0  0.0  0.0  0.0   0.0
+  'c'  0.0  0.0  0.0  0.0   0.0

For simplicity, the same Dimension types are also used as wrappers in getindex, like:

julia
x = A[X(2), Y(3)]
+
+# output
+
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ 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  0.0
+ 2021-02-01T00:00:00  0.0
+ 2021-03-01T00:00:00  0.0
+ 2021-04-01T00:00:00  0.0
+ 2021-05-01T00:00:00  0.0
+ 2021-06-01T00:00:00  0.0
+ 2021-07-01T00:00:00  0.0
+ 2021-08-01T00:00:00  0.0
+ 2021-09-01T00:00:00  0.0
+ 2021-10-01T00:00:00  0.0
+ 2021-11-01T00:00:00  0.0
+ 2021-12-01T00:00:00  0.0

A Dimension can also wrap Selector.

julia
x = A[X(Between(3, 4)), Y(At('b'))]
+
+# output
+
+╭──────────────────────────╮
+│ 1×12 DimArray{Float64,2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 4:2:4 ForwardOrdered Regular Points,
+  → 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
+
+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
+
+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
+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}}
+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))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(true, true, true)
+
+julia> hasdim(A, Ti)
+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
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> commondims(A, X)
+(↓ X)
+
+julia> commondims(A, (X, Z))
+(↓ X, → Z)
+
+julia> commondims(A, Ti)
+()

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
+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}))
+
+# output
+╭───────────────────────────╮
+│ 2×4×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ a, → b, ↗ c
+└───────────────────────────┘
+[:, :, 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...)
+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
+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


+ + + + \ No newline at end of file diff --git a/previews/PR801/api/lookuparrays.html b/previews/PR801/api/lookuparrays.html new file mode 100644 index 000000000..a119ce3ab --- /dev/null +++ b/previews/PR801/api/lookuparrays.html @@ -0,0 +1,242 @@ + + + + + + Lookups | DimensionalData.jl + + + + + + + + + + + + + + +
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
+
+x = X(Sampled(100:-20:10; sampling=Intervals(Start())))
+y = Y(Sampled([1, 4, 7, 10]; span=Regular(3), sampling=Intervals(Start())))
+A = ones(x, y)
+
+# output
+╭─────────────────────────╮
+│ 5×4 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 100:-20:20 ReverseOrdered Regular Intervals{Start},
+  → Y Sampled{Int64} [1, 4, 7, 10] ForwardOrdered Regular Intervals{Start}
+└──────────────────────────────────────────────────────────────────────────┘
+   ↓ →  1    4    7    10
+ 100    1.0  1.0  1.0   1.0
+  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
+
+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
+
+ds = X(["one", "two", "three"]), Y([:a, :b, :c, :d])
+A = DimArray(rand(3, 4), ds)
+Dimensions.lookup(A)
+
+# 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
+
+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
+
+m = LinearMap([0.5 0.0; 0.0 0.5])
+A = [1 2  3  4
+     5 6  7  8
+     9 10 11 12];
+da = DimArray(A, (X(Transformed(m)), Y(Transformed(m))))
+
+da[X(At(6.0)), Y(At(2.0))]
+
+# output
+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
+
+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
+
+A = DimArray(rand(3, 3), (X, Y))
+Dimensions.lookup(A)
+
+# output
+
+NoLookup, NoLookup

Which is identical to:

julia
using .Lookups
+A = DimArray(rand(3, 3), (X(NoLookup()), Y(NoLookup())))
+Dimensions.lookup(A)
+
+# output
+
+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}}}
+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
+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
+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
+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
+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
+
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(At(20)), Y(At(6))]
+
+# output
+
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Near(23)), Y(Near(5.1))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Between(15, 25)), Y(Between(4, 6.5))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Touches(15, 25)), Y(Touches(4, 6.5))]
+
+# output
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+dims_ = X(10:10:20; sampling=DD.Intervals(DD.Center())),
+        Y(5:7; sampling=DD.Intervals(DD.Center()))
+A = DimArray([1 2 3; 4 5 6], dims_)
+A[X(Contains(8)), Y(Contains(6.8))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(19:21)))
+A[X(Where(x -> x > 15)), Y(Where(x -> x in (19, 21)))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴─────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} [20] ForwardOrdered Irregular Points,
+  → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+  ↓ →  19  21
+ 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
+
+dimz = X(10.0:20:200.0), Ti(1u"s":5u"s":100u"s")
+A = DimArray((1:10) * (1:20)', dimz)
+A[X=All(At(10.0), At(50.0)), Ti=All(1u"s"..10u"s", 90u"s"..100u"s")]
+
+# output
+
+╭───────────────────────╮
+│ 2×4 DimArray{Int64,2} │
+├───────────────────────┴──────────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Float64} [10.0, 50.0] ForwardOrdered Irregular Points,
+  → Ti Sampled{Unitful.Quantity{Int64, 𝐓, Unitful.FreeUnits{(s,), 𝐓, nothing}}} [1 s, 6 s, 91 s, 96 s] ForwardOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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


+ + + + \ No newline at end of file diff --git a/previews/PR801/api/reference.html b/previews/PR801/api/reference.html new file mode 100644 index 000000000..b61e6e502 --- /dev/null +++ b/previews/PR801/api/reference.html @@ -0,0 +1,487 @@ + + + + + + API Reference | DimensionalData.jl + + + + + + + + + + + + + + +
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
+
+julia> ti = Ti(DateTime(2001):Month(1):DateTime(2001,12));
+
+julia> x = X(10:10:100);
+
+julia> A = DimArray(rand(12,10), (ti, x), name="example");
+
+julia> A[X(Near([12, 35])), Ti(At(DateTime(2001,5)))]
+╭───────────────────────────────────────╮
+│ 2-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴─────────────── dims ┐
+  ↓ X Sampled{Int64} [10, 40] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+ 10  0.253849
+ 40  0.637077
+
+julia> A[Near(DateTime(2001, 5, 4)), Between(20, 50)]
+╭───────────────────────────────────────╮
+│ 4-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴───────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:50 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────┘
+ 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
+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))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 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
+
+julia> rand(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 1  0  0  1
+
+julia> rand(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+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))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 0  0  0  0
+
+julia> zeros(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+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))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 1  1  1  1
+ 1  1  1  1
+
+julia> ones(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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}}
+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))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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)
+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
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(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
+
+DimStack(data::AbstractDimArray...; kw...)
+DimStack(data::Tuple{Vararg{AbstractDimArray}}; kw...)
+DimStack(data::NamedTuple{Keys,Vararg{AbstractDimArray}}; kw...)
+DimStack(data::NamedTuple, dims::DimTuple; metadata=NoMetadata(); kw...)

DimStack holds multiple objects sharing some dimensions, in a NamedTuple.

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 or view with Int, Dimensions or Selectors that resolve to Int will return a NamedTuple of values from each layer in the stack. This has very good performance, and avoids the need to always use map.

  • getindex or view with a Vector or Colon will return another DimStack where all data layers have been sliced.

  • setindex! must pass a Tuple or NamedTuple matching the layers.

  • many base and Statistics methods (sum, mean etc) will work as for a DimArray again removing the need to use map.

julia
function DimStack(A::AbstractDimArray;
+    layersfrom=nothing, name=nothing, metadata=metadata(A), refdims=refdims(A), kw...
+)

For example, here we take the mean over the time dimension for all layers:

julia
mean(mydimstack; dims=Ti)

And this equivalent to:

julia
map(A -> mean(A; dims=Ti), mydimstack)

This design gives 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.

DimStack can be constructed from multiple AbstractDimArray or a NamedTuple of AbstractArray and a matching dims tuple.

Most Base and Statistics methods that apply to AbstractArray can be used on all layers of the stack simulataneously. The result is a DimStack, or a NamedTuple if methods like mean are used without dims arguments, and return a single non-array value.

Example

julia
julia> using DimensionalData
+
+julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0];
+
+julia> dimz = (X([:a, :b]), Y(10.0:10.0:30.0))
+(↓ X [:a, :b],
+→ Y 10.0:10.0:30.0)
+
+julia> da1 = DimArray(1A, dimz; name=:one);
+
+julia> da2 = DimArray(2A, dimz; name=:two);
+
+julia> da3 = DimArray(3A, dimz; name=:three);
+
+julia> s = DimStack(da1, da2, da3);
+
+julia> s[At(:b), At(10.0)]
+(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
+
+DimIndices(x)
+DimIndices(dims::Tuple)
+DimIndices(dims::Dimension)

Like CartesianIndices, but for Dimensions. Behaves as an Array of Tuple of Dimension(i) for all combinations of the axis indices of dims.

This can be used to view/index into arbitrary dimensions over an array, and is especially useful when combined with otherdims, to iterate over the indices of unknown dimension.

DimIndices can be used directly in getindex like CartesianIndices, and freely mixed with individual Dimensions or tuples of Dimension.

Example

Index a DimArray with DimIndices.

Notice that unlike CartesianIndices, it doesn't matter if the dimensions are not in the same order. Or even if they are not all contained in each.

julia
julia> A = rand(Y(0.0:0.3:1.0), X('a':'f'))
+╭─────────────────────────╮
+│ 4×6 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.3:0.9 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':1:'f' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'b'       'c'        'd'        'e'       'f'
+ 0.0  0.9063    0.253849  0.0991336  0.0320967  0.774092  0.893537
+ 0.3  0.443494  0.334152  0.125287   0.350546   0.183555  0.354868
+ 0.6  0.745673  0.427328  0.692209   0.930332   0.297023  0.131798
+ 0.9  0.512083  0.867547  0.136551   0.959434   0.150155  0.941133
+
+julia> di = DimIndices((X(1:2:4), Y(1:2:4)))
+╭─────────────────────────────────────────────╮
+│ 2×2 DimIndices{Tuple{X{Int64}, Y{Int64}},2} │
+├─────────────────────────────────────── dims ┤
+  ↓ X 1:2:3,
+  → Y 1:2:3
+└─────────────────────────────────────────────┘
+ ↓ →  1                3
+ 1     (↓ X 1, → Y 1)   (↓ X 1, → Y 3)
+ 3     (↓ X 3, → Y 1)   (↓ X 3, → Y 3)
+
+julia> A[di] # Index A with these indices
+╭─────────────────────────╮
+│ 2×2 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.6:0.6 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':2:'c' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'c'
+ 0.0  0.9063    0.0991336
+ 0.6  0.745673  0.692209

source


# DimensionalData.DimSelectors — Type.
julia
DimSelectors <: AbstractArray
+
+DimSelectors(x; selectors, atol...)
+DimSelectors(dims::Tuple; selectors, atol...)
+DimSelectors(dims::Dimension; selectors, atol...)

Like DimIndices, but returns Dimensions holding the chosen Selectors.

Indexing into another AbstractDimArray with DimSelectors is similar to doing an interpolation.

Keywords

  • selectors: Near, At or Contains, or a mixed tuple of these. At is the default, meaning only exact or within atol values are used.

  • atol: used for At selectors only, as the atol value.

Example

Here we can interpolate a DimArray to the lookups of another DimArray using DimSelectors with Near. This is essentially equivalent to nearest neighbour interpolation.

julia
julia> A = rand(X(1.0:3.0:30.0), Y(1.0:5.0:30.0), Ti(1:2));
+
+julia> target = rand(X(1.0:10.0:30.0), Y(1.0:10.0:30.0));
+
+julia> A[DimSelectors(target; selectors=Near), Ti=2]
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} [1.0, 10.0, 22.0] ForwardOrdered Irregular Points,
+  → Y Sampled{Float64} [1.0, 11.0, 21.0] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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
+
+DimTable(s::AbstractDimStack; mergedims=nothing)
+DimTable(x::AbstractDimArray; layersfrom=nothing, mergedims=nothing)
+DimTable(xs::Vararg{AbstractDimArray}; layernames=nothing, mergedims=nothing)

Construct a Tables.jl/TableTraits.jl compatible object out of an AbstractDimArray or AbstractDimStack.

This table will have columns for the array data and columns for each Dimension index, as a [DimColumn]. These are lazy, and generated as required.

Column names are converted from the dimension types using DimensionalData.name. This means type Ti becomes the column name :Ti, and Dim{:custom} becomes :custom.

To get dimension columns, you can index with Dimension (X()) or Dimension type (X) as well as the regular Int or Symbol.

Keywords

  • mergedims: Combine two or more dimensions into a new dimension.

  • layersfrom: Treat a dimension of an AbstractDimArray as layers of an AbstractDimStack.

Example

julia
julia> using DimensionalData, Tables
+
+julia> a = DimArray(ones(16, 16, 3), (X, Y, Dim{:band}))
+╭─────────────────────────────╮
+│ 16×16×3 DimArray{Float64,3} │
+├─────────────────────── dims ┤
+  ↓ X, → Y, ↗ band
+└─────────────────────────────┘
+[:, :, 1]
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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...)
+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)));
+
+julia> groups = groupby(A, Ti => month) # Group by month
+╭───────────────────────────────────────────────────╮
+│ 12-element DimGroupByArray{DimArray{Float64,2},1} │
+├───────────────────────────────────────────────────┴───────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+├─────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Y, ↗ Ti
+└───────────────────────────────────────────────────────────────────────┘
+  1  191×20×32 DimArray
+  2  191×20×28 DimArray
+  3  191×20×31 DimArray
+  ⋮
+ 11  191×20×30 DimArray
+ 12  191×20×31 DimArray

And take the mean:

julia
julia> groupmeans = mean.(groups) # Take the monthly mean
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+└───────────────────────────────────────────────────────────────────────┘
+  1  0.500064
+  2  0.499762
+  3  0.500083
+  4  0.499985
+  ⋮
+ 10  0.500874
+ 11  0.498704
+ 12  0.50047

Calculate daily anomalies from the monthly mean. Notice we map a broadcast .- rather than -. This is because the size of the arrays to not match after application of mean.

julia
julia> map(.-, groupby(A, Ti=>month), mean.(groupby(A, Ti=>month), dims=Ti));

Or do something else with Y:

julia
julia> groupmeans = mean.(groupby(A, Ti=>month, Y=>isodd))
+╭──────────────────────────╮
+│ 12×2 DimArray{Float64,2} │
+├──────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points,
+  → Y  Sampled{Bool} [false, true] ForwardOrdered Irregular Points
+├────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => (:Ti=>month, :Y=>isodd)
+└────────────────────────────────────────────────────────────────────────┘
+  ↓ →  false         true
+  1        0.499594     0.500533
+  2        0.498145     0.501379
+  ⋮
+ 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)
+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
+
+set(dim::Dimension, index::AbstractArray) => Dimension
+set(dim::Dimension, lookup::Lookup) => Dimension
+set(dim::Dimension, lookupcomponent::LookupTrait) => Dimension
+set(dim::Dimension, metadata::AbstractMetadata) => Dimension

Set the properties of an object, its internal data or the traits of its dimensions and lookup index.

As DimensionalData is so strongly typed you do not need to specify what field of a Lookup to set - there is no ambiguity.

To set fields of a Lookup you need to specify the dimension. This can be done using X => val pairs, X = val keyword arguments, or X(val) wrapped arguments.

You can also set the fields of all dimensions by simply passing a single Lookup or lookup trait - it will be set for all dimensions.

When a Dimension or Lookup is passed to set to replace the existing ones, fields that are not set will keep their original values.

Notes:

Changing a lookup index range/vector will also update the step size and order where applicable.

Setting the Order like ForwardOrdered will not reverse the array or dimension to match. Use reverse and reorder to do this.

Examples

julia
julia> using DimensionalData; const DD = DimensionalData;
+
+julia> da = DimArray(zeros(3, 4), (custom=10.0:010.0:30.0, Z=-20:010.0:10.0));
+
+julia> set(da, ones(3, 4))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -20.0  -10.0  0.0  10.0
+ 10.0    1.0    1.0  1.0   1.0
+ 20.0    1.0    1.0  1.0   1.0
+ 30.0    1.0    1.0  1.0   1.0

Change the Dimension wrapper type:

julia
julia> set(da, :Z => Ti, :custom => Z)
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Z  Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Ti Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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

Change the lookup Vector:

julia
julia> set(da, Z => [:a, :b, :c, :d], :custom => [4, 5, 6])
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Int64} [4, 5, 6] ForwardOrdered Regular Points,
+  → Z      Sampled{Symbol} [:a, :b, :c, :d] ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+ ↓ →   :a   :b   :c   :d
+ 4    0.0  0.0  0.0  0.0
+ 5    0.0  0.0  0.0  0.0
+ 6    0.0  0.0  0.0  0.0

Change the Lookup type:

julia
julia> set(da, Z=DD.NoLookup(), custom=DD.Sampled())
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z
+└─────────────────────────────────────────────────────────────────────────┘
+ 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

Change the Sampling trait:

julia
julia> set(da, :custom => DD.Irregular(10, 12), Z => DD.Regular(9.9))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Irregular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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
+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
+da1 = ones(X(3))
+da2 = fill(2, Y(4), X(3))
+
+@d da1 .* da2
+@d da1 .* da2 .+ 5 dims=(Y, X)
+@d da1 .* da2 .+ 5 (dims=(Y, X), strict=false, name=:testname)

Use with @.

@d does not imply @.. You need to specify each broadcast. But @. can be used with @d as the inner macro.

julia
using DimensionalData
+da1 = ones(X(3))
+da2 = fill(2, Y(4), X(3))
+
+@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
+
+julia> ds = (X(0:0.1:0.4), Y(10:10:100), Ti([0, 3, 4]))
+(↓ X  0.0:0.1:0.4,
+→ Y  10:10:100,
+↗ Ti [0, 3, 4])
+
+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...)
+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
+
+# Create a DimArray
+da = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(300:-100:100)))
+
+# Reverse it
+rev = reverse(da, dims=Y)
+
+# using `da` in reorder will return it to the original order
+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((
+           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))
+       ));
+
+julia> slices = eachslice(ds; dims=(Z, X));
+
+julia> size(slices)
+(4, 2)
+
+julia> map(dims, axes(slices))
+(↓ Z Base.OneTo(4),
+→ X Base.OneTo(2))
+
+julia> first(slices)
+╭──────────────╮
+│ 3×5 DimStack │
+├──────────────┴─────────────────────────────────── dims ┐
+  ↓ Y  Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  → Ti
+├──────────────────────────────────────────────── 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
+
+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
+
+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


+ + + + \ No newline at end of file diff --git a/previews/PR801/assets/api_dimensions.md.MWHKMaKb.js b/previews/PR801/assets/api_dimensions.md.MWHKMaKb.js new file mode 100644 index 000000000..60fec6def --- /dev/null +++ b/previews/PR801/assets/api_dimensions.md.MWHKMaKb.js @@ -0,0 +1,157 @@ +import{_ as s,c as a,a4 as n,o as e}from"./chunks/framework.D3Ia4FEQ.js";const g=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),t={name:"api/dimensions.md"};function l(h,i,p,k,d,r){return e(),a("div",null,i[0]||(i[0]=[n(`

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'])
+ti = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
+
+A = DimArray(zeros(3, 5, 12), (y, x, ti))
+
+# output
+
+╭────────────────────────────╮
+│ 3×5×12 DimArray{Float64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Y  Categorical{Char} ['a', 'b', 'c'] ForwardOrdered,
+  → X  Sampled{Int64} 2:2:10 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+[:, :, 1]
+ ↓ →   2    4    6    8    10
+  'a'  0.0  0.0  0.0  0.0   0.0
+  'b'  0.0  0.0  0.0  0.0   0.0
+  'c'  0.0  0.0  0.0  0.0   0.0

For simplicity, the same Dimension types are also used as wrappers in getindex, like:

julia
x = A[X(2), Y(3)]
+
+# output
+
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ 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  0.0
+ 2021-02-01T00:00:00  0.0
+ 2021-03-01T00:00:00  0.0
+ 2021-04-01T00:00:00  0.0
+ 2021-05-01T00:00:00  0.0
+ 2021-06-01T00:00:00  0.0
+ 2021-07-01T00:00:00  0.0
+ 2021-08-01T00:00:00  0.0
+ 2021-09-01T00:00:00  0.0
+ 2021-10-01T00:00:00  0.0
+ 2021-11-01T00:00:00  0.0
+ 2021-12-01T00:00:00  0.0

A Dimension can also wrap Selector.

julia
x = A[X(Between(3, 4)), Y(At('b'))]
+
+# output
+
+╭──────────────────────────╮
+│ 1×12 DimArray{Float64,2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 4:2:4 ForwardOrdered Regular Points,
+  → 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
+
+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
+
+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
+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}}
+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

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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

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));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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

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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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

Check if an object or tuple contains an Dimension, or a tuple of dimensions.

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(true, true, true)
+
+julia> hasdim(A, Ti)
+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
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> commondims(A, X)
+(↓ X)
+
+julia> commondims(A, (X, Z))
+(↓ X, → Z)
+
+julia> commondims(A, Ti)
+()

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
+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

Example

julia
using DimensionalData
+A = ones(X(2), Y(4), Z(2))
+Dimensions.swapdims(A, (Dim{:a}, Dim{:b}, Dim{:c}))
+
+# output
+╭───────────────────────────╮
+│ 2×4×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ a, → b, ↗ c
+└───────────────────────────┘
+[:, :, 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

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:

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

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


`,81)]))}const E=s(t,[["render",l]]);export{g as __pageData,E as default}; diff --git a/previews/PR801/assets/api_dimensions.md.MWHKMaKb.lean.js b/previews/PR801/assets/api_dimensions.md.MWHKMaKb.lean.js new file mode 100644 index 000000000..60fec6def --- /dev/null +++ b/previews/PR801/assets/api_dimensions.md.MWHKMaKb.lean.js @@ -0,0 +1,157 @@ +import{_ as s,c as a,a4 as n,o as e}from"./chunks/framework.D3Ia4FEQ.js";const g=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"api/dimensions.md","filePath":"api/dimensions.md","lastUpdated":null}'),t={name:"api/dimensions.md"};function l(h,i,p,k,d,r){return e(),a("div",null,i[0]||(i[0]=[n(`

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'])
+ti = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))
+
+A = DimArray(zeros(3, 5, 12), (y, x, ti))
+
+# output
+
+╭────────────────────────────╮
+│ 3×5×12 DimArray{Float64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Y  Categorical{Char} ['a', 'b', 'c'] ForwardOrdered,
+  → X  Sampled{Int64} 2:2:10 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Dates.DateTime} Dates.DateTime("2021-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2021-12-01T00:00:00") ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+[:, :, 1]
+ ↓ →   2    4    6    8    10
+  'a'  0.0  0.0  0.0  0.0   0.0
+  'b'  0.0  0.0  0.0  0.0   0.0
+  'c'  0.0  0.0  0.0  0.0   0.0

For simplicity, the same Dimension types are also used as wrappers in getindex, like:

julia
x = A[X(2), Y(3)]
+
+# output
+
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ 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  0.0
+ 2021-02-01T00:00:00  0.0
+ 2021-03-01T00:00:00  0.0
+ 2021-04-01T00:00:00  0.0
+ 2021-05-01T00:00:00  0.0
+ 2021-06-01T00:00:00  0.0
+ 2021-07-01T00:00:00  0.0
+ 2021-08-01T00:00:00  0.0
+ 2021-09-01T00:00:00  0.0
+ 2021-10-01T00:00:00  0.0
+ 2021-11-01T00:00:00  0.0
+ 2021-12-01T00:00:00  0.0

A Dimension can also wrap Selector.

julia
x = A[X(Between(3, 4)), Y(At('b'))]
+
+# output
+
+╭──────────────────────────╮
+│ 1×12 DimArray{Float64,2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 4:2:4 ForwardOrdered Regular Points,
+  → 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
+
+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
+
+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
+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}}
+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

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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

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));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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

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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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

Check if an object or tuple contains an Dimension, or a tuple of dimensions.

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(true, true, true)
+
+julia> hasdim(A, Ti)
+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
+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
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> commondims(A, X)
+(↓ X)
+
+julia> commondims(A, (X, Z))
+(↓ X, → Z)
+
+julia> commondims(A, Ti)
+()

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
+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

Example

julia
using DimensionalData
+A = ones(X(2), Y(4), Z(2))
+Dimensions.swapdims(A, (Dim{:a}, Dim{:b}, Dim{:c}))
+
+# output
+╭───────────────────────────╮
+│ 2×4×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ a, → b, ↗ c
+└───────────────────────────┘
+[:, :, 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

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:

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

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


`,81)]))}const E=s(t,[["render",l]]);export{g as __pageData,E as default}; diff --git a/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.js b/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.js new file mode 100644 index 000000000..9a9365fc7 --- /dev/null +++ b/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.js @@ -0,0 +1,218 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.D3Ia4FEQ.js";const c=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),t={name:"api/lookuparrays.md"};function l(p,s,o,h,r,k){return n(),a("div",null,s[0]||(s[0]=[e(`

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

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
+
+x = X(Sampled(100:-20:10; sampling=Intervals(Start())))
+y = Y(Sampled([1, 4, 7, 10]; span=Regular(3), sampling=Intervals(Start())))
+A = ones(x, y)
+
+# output
+╭─────────────────────────╮
+│ 5×4 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 100:-20:20 ReverseOrdered Regular Intervals{Start},
+  → Y Sampled{Int64} [1, 4, 7, 10] ForwardOrdered Regular Intervals{Start}
+└──────────────────────────────────────────────────────────────────────────┘
+   ↓ →  1    4    7    10
+ 100    1.0  1.0  1.0   1.0
+  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
+
+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

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

Example

Create an array with [Interval] sampling.

julia
using DimensionalData
+
+ds = X(["one", "two", "three"]), Y([:a, :b, :c, :d])
+A = DimArray(rand(3, 4), ds)
+Dimensions.lookup(A)
+
+# 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
+
+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

Keyword Arguments

Example

julia
using DimensionalData, DimensionalData.Lookups, CoordinateTransformations
+
+m = LinearMap([0.5 0.0; 0.0 0.5])
+A = [1 2  3  4
+     5 6  7  8
+     9 10 11 12];
+da = DimArray(A, (X(Transformed(m)), Y(Transformed(m))))
+
+da[X(At(6.0)), Y(At(2.0))]
+
+# output
+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

Keywords

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
+
+A = DimArray(rand(3, 3), (X, Y))
+Dimensions.lookup(A)
+
+# output
+
+NoLookup, NoLookup

Which is identical to:

julia
using .Lookups
+A = DimArray(rand(3, 3), (X(NoLookup()), Y(NoLookup())))
+Dimensions.lookup(A)
+
+# output
+
+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}}}
+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
+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
+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
+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
+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:

And IntervalSets.jl Interval can be used instead of Between

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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(At(20)), Y(At(6))]
+
+# output
+
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Near(23)), Y(Near(5.1))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Between(15, 25)), Y(Between(4, 6.5))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Touches(15, 25)), Y(Touches(4, 6.5))]
+
+# output
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+dims_ = X(10:10:20; sampling=DD.Intervals(DD.Center())),
+        Y(5:7; sampling=DD.Intervals(DD.Center()))
+A = DimArray([1 2 3; 4 5 6], dims_)
+A[X(Contains(8)), Y(Contains(6.8))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(19:21)))
+A[X(Where(x -> x > 15)), Y(Where(x -> x in (19, 21)))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴─────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} [20] ForwardOrdered Irregular Points,
+  → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+  ↓ →  19  21
+ 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
+
+dimz = X(10.0:20:200.0), Ti(1u"s":5u"s":100u"s")
+A = DimArray((1:10) * (1:20)', dimz)
+A[X=All(At(10.0), At(50.0)), Ti=All(1u"s"..10u"s", 90u"s"..100u"s")]
+
+# output
+
+╭───────────────────────╮
+│ 2×4 DimArray{Int64,2} │
+├───────────────────────┴──────────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Float64} [10.0, 50.0] ForwardOrdered Irregular Points,
+  → Ti Sampled{Unitful.Quantity{Int64, 𝐓, Unitful.FreeUnits{(s,), 𝐓, nothing}}} [1 s, 6 s, 91 s, 96 s] ForwardOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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


`,126)]))}const g=i(t,[["render",l]]);export{c as __pageData,g as default}; diff --git a/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.lean.js b/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.lean.js new file mode 100644 index 000000000..9a9365fc7 --- /dev/null +++ b/previews/PR801/assets/api_lookuparrays.md.BF14Z7at.lean.js @@ -0,0 +1,218 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.D3Ia4FEQ.js";const c=JSON.parse('{"title":"Lookups","description":"","frontmatter":{},"headers":[],"relativePath":"api/lookuparrays.md","filePath":"api/lookuparrays.md","lastUpdated":null}'),t={name:"api/lookuparrays.md"};function l(p,s,o,h,r,k){return n(),a("div",null,s[0]||(s[0]=[e(`

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

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
+
+x = X(Sampled(100:-20:10; sampling=Intervals(Start())))
+y = Y(Sampled([1, 4, 7, 10]; span=Regular(3), sampling=Intervals(Start())))
+A = ones(x, y)
+
+# output
+╭─────────────────────────╮
+│ 5×4 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 100:-20:20 ReverseOrdered Regular Intervals{Start},
+  → Y Sampled{Int64} [1, 4, 7, 10] ForwardOrdered Regular Intervals{Start}
+└──────────────────────────────────────────────────────────────────────────┘
+   ↓ →  1    4    7    10
+ 100    1.0  1.0  1.0   1.0
+  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
+
+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

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

Example

Create an array with [Interval] sampling.

julia
using DimensionalData
+
+ds = X(["one", "two", "three"]), Y([:a, :b, :c, :d])
+A = DimArray(rand(3, 4), ds)
+Dimensions.lookup(A)
+
+# 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
+
+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

Keyword Arguments

Example

julia
using DimensionalData, DimensionalData.Lookups, CoordinateTransformations
+
+m = LinearMap([0.5 0.0; 0.0 0.5])
+A = [1 2  3  4
+     5 6  7  8
+     9 10 11 12];
+da = DimArray(A, (X(Transformed(m)), Y(Transformed(m))))
+
+da[X(At(6.0)), Y(At(2.0))]
+
+# output
+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

Keywords

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
+
+A = DimArray(rand(3, 3), (X, Y))
+Dimensions.lookup(A)
+
+# output
+
+NoLookup, NoLookup

Which is identical to:

julia
using .Lookups
+A = DimArray(rand(3, 3), (X(NoLookup()), Y(NoLookup())))
+Dimensions.lookup(A)
+
+# output
+
+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}}}
+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
+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
+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
+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
+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:

And IntervalSets.jl Interval can be used instead of Between

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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(At(20)), Y(At(6))]
+
+# output
+
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Near(23)), Y(Near(5.1))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Between(15, 25)), Y(Between(4, 6.5))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(5:7)))
+A[X(Touches(15, 25)), Y(Touches(4, 6.5))]
+
+# output
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:20 ForwardOrdered Regular Points,
+  → Y Sampled{Int64} 5:6 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────┘
+  ↓ →  5  6
+ 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
+dims_ = X(10:10:20; sampling=DD.Intervals(DD.Center())),
+        Y(5:7; sampling=DD.Intervals(DD.Center()))
+A = DimArray([1 2 3; 4 5 6], dims_)
+A[X(Contains(8)), Y(Contains(6.8))]
+
+# output
+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
+
+A = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(19:21)))
+A[X(Where(x -> x > 15)), Y(Where(x -> x in (19, 21)))]
+
+# output
+
+╭───────────────────────╮
+│ 1×2 DimArray{Int64,2} │
+├───────────────────────┴─────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} [20] ForwardOrdered Irregular Points,
+  → Y Sampled{Int64} [19, 21] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+  ↓ →  19  21
+ 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
+
+dimz = X(10.0:20:200.0), Ti(1u"s":5u"s":100u"s")
+A = DimArray((1:10) * (1:20)', dimz)
+A[X=All(At(10.0), At(50.0)), Ti=All(1u"s"..10u"s", 90u"s"..100u"s")]
+
+# output
+
+╭───────────────────────╮
+│ 2×4 DimArray{Int64,2} │
+├───────────────────────┴──────────────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Float64} [10.0, 50.0] ForwardOrdered Irregular Points,
+  → Ti Sampled{Unitful.Quantity{Int64, 𝐓, Unitful.FreeUnits{(s,), 𝐓, nothing}}} [1 s, 6 s, 91 s, 96 s] ForwardOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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
+
+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


`,126)]))}const g=i(t,[["render",l]]);export{c as __pageData,g as default}; diff --git a/previews/PR801/assets/api_reference.md.CQevsPbp.js b/previews/PR801/assets/api_reference.md.CQevsPbp.js new file mode 100644 index 000000000..e9903d3c9 --- /dev/null +++ b/previews/PR801/assets/api_reference.md.CQevsPbp.js @@ -0,0 +1,463 @@ +import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.D3Ia4FEQ.js";const E=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),t={name:"api/reference.md"};function e(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`

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

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
+
+julia> ti = Ti(DateTime(2001):Month(1):DateTime(2001,12));
+
+julia> x = X(10:10:100);
+
+julia> A = DimArray(rand(12,10), (ti, x), name="example");
+
+julia> A[X(Near([12, 35])), Ti(At(DateTime(2001,5)))]
+╭───────────────────────────────────────╮
+│ 2-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴─────────────── dims ┐
+  ↓ X Sampled{Int64} [10, 40] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+ 10  0.253849
+ 40  0.637077
+
+julia> A[Near(DateTime(2001, 5, 4)), Between(20, 50)]
+╭───────────────────────────────────────╮
+│ 4-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴───────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:50 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────┘
+ 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
+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:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData, Random; Random.seed!(123);
+
+julia> rand(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 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:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> rand(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 1  0  0  1
+
+julia> rand(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+Base.zeros(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray

Create a DimArray of zeros.

There are two kinds of Dimension value acepted:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> zeros(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 0  0  0  0
+
+julia> zeros(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+Base.ones(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray

Create a DimArray of ones.

There are two kinds of Dimension value acepted:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> ones(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 1  1  1  1
+ 1  1  1  1
+
+julia> ones(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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}}
+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

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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)
+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
+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

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));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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

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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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

Check if an object or tuple contains an Dimension, or a tuple of dimensions.

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(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:

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...)
+DimStack(data::NamedTuple{Keys,Vararg{AbstractDimArray}}; kw...)
+DimStack(data::NamedTuple, dims::DimTuple; metadata=NoMetadata(); kw...)

DimStack holds multiple objects sharing some dimensions, in a NamedTuple.

Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:

julia
function DimStack(A::AbstractDimArray;
+    layersfrom=nothing, name=nothing, metadata=metadata(A), refdims=refdims(A), kw...
+)

For example, here we take the mean over the time dimension for all layers:

julia
mean(mydimstack; dims=Ti)

And this equivalent to:

julia
map(A -> mean(A; dims=Ti), mydimstack)

This design gives 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.

DimStack can be constructed from multiple AbstractDimArray or a NamedTuple of AbstractArray and a matching dims tuple.

Most Base and Statistics methods that apply to AbstractArray can be used on all layers of the stack simulataneously. The result is a DimStack, or a NamedTuple if methods like mean are used without dims arguments, and return a single non-array value.

Example

julia
julia> using DimensionalData
+
+julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0];
+
+julia> dimz = (X([:a, :b]), Y(10.0:10.0:30.0))
+(↓ X [:a, :b],
+→ Y 10.0:10.0:30.0)
+
+julia> da1 = DimArray(1A, dimz; name=:one);
+
+julia> da2 = DimArray(2A, dimz; name=:two);
+
+julia> da3 = DimArray(3A, dimz; name=:three);
+
+julia> s = DimStack(da1, da2, da3);
+
+julia> s[At(:b), At(10.0)]
+(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
+
+DimIndices(x)
+DimIndices(dims::Tuple)
+DimIndices(dims::Dimension)

Like CartesianIndices, but for Dimensions. Behaves as an Array of Tuple of Dimension(i) for all combinations of the axis indices of dims.

This can be used to view/index into arbitrary dimensions over an array, and is especially useful when combined with otherdims, to iterate over the indices of unknown dimension.

DimIndices can be used directly in getindex like CartesianIndices, and freely mixed with individual Dimensions or tuples of Dimension.

Example

Index a DimArray with DimIndices.

Notice that unlike CartesianIndices, it doesn't matter if the dimensions are not in the same order. Or even if they are not all contained in each.

julia
julia> A = rand(Y(0.0:0.3:1.0), X('a':'f'))
+╭─────────────────────────╮
+│ 4×6 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.3:0.9 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':1:'f' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'b'       'c'        'd'        'e'       'f'
+ 0.0  0.9063    0.253849  0.0991336  0.0320967  0.774092  0.893537
+ 0.3  0.443494  0.334152  0.125287   0.350546   0.183555  0.354868
+ 0.6  0.745673  0.427328  0.692209   0.930332   0.297023  0.131798
+ 0.9  0.512083  0.867547  0.136551   0.959434   0.150155  0.941133
+
+julia> di = DimIndices((X(1:2:4), Y(1:2:4)))
+╭─────────────────────────────────────────────╮
+│ 2×2 DimIndices{Tuple{X{Int64}, Y{Int64}},2} │
+├─────────────────────────────────────── dims ┤
+  ↓ X 1:2:3,
+  → Y 1:2:3
+└─────────────────────────────────────────────┘
+ ↓ →  1                3
+ 1     (↓ X 1, → Y 1)   (↓ X 1, → Y 3)
+ 3     (↓ X 3, → Y 1)   (↓ X 3, → Y 3)
+
+julia> A[di] # Index A with these indices
+╭─────────────────────────╮
+│ 2×2 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.6:0.6 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':2:'c' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'c'
+ 0.0  0.9063    0.0991336
+ 0.6  0.745673  0.692209

source


# DimensionalData.DimSelectors — Type.
julia
DimSelectors <: AbstractArray
+
+DimSelectors(x; selectors, atol...)
+DimSelectors(dims::Tuple; selectors, atol...)
+DimSelectors(dims::Dimension; selectors, atol...)

Like DimIndices, but returns Dimensions holding the chosen Selectors.

Indexing into another AbstractDimArray with DimSelectors is similar to doing an interpolation.

Keywords

Example

Here we can interpolate a DimArray to the lookups of another DimArray using DimSelectors with Near. This is essentially equivalent to nearest neighbour interpolation.

julia
julia> A = rand(X(1.0:3.0:30.0), Y(1.0:5.0:30.0), Ti(1:2));
+
+julia> target = rand(X(1.0:10.0:30.0), Y(1.0:10.0:30.0));
+
+julia> A[DimSelectors(target; selectors=Near), Ti=2]
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} [1.0, 10.0, 22.0] ForwardOrdered Irregular Points,
+  → Y Sampled{Float64} [1.0, 11.0, 21.0] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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

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)
+DimTable(xs::Vararg{AbstractDimArray}; layernames=nothing, mergedims=nothing)

Construct a Tables.jl/TableTraits.jl compatible object out of an AbstractDimArray or AbstractDimStack.

This table will have columns for the array data and columns for each Dimension index, as a [DimColumn]. These are lazy, and generated as required.

Column names are converted from the dimension types using DimensionalData.name. This means type Ti becomes the column name :Ti, and Dim{:custom} becomes :custom.

To get dimension columns, you can index with Dimension (X()) or Dimension type (X) as well as the regular Int or Symbol.

Keywords

Example

julia
julia> using DimensionalData, Tables
+
+julia> a = DimArray(ones(16, 16, 3), (X, Y, Dim{:band}))
+╭─────────────────────────────╮
+│ 16×16×3 DimArray{Float64,3} │
+├─────────────────────── dims ┤
+  ↓ X, → Y, ↗ band
+└─────────────────────────────┘
+[:, :, 1]
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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...)
+groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Dimension{<:Callable}...)

Group A by grouping functions or Bins over multiple dimensions.

Arguments

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)));
+
+julia> groups = groupby(A, Ti => month) # Group by month
+╭───────────────────────────────────────────────────╮
+│ 12-element DimGroupByArray{DimArray{Float64,2},1} │
+├───────────────────────────────────────────────────┴───────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+├─────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Y, ↗ Ti
+└───────────────────────────────────────────────────────────────────────┘
+  1  191×20×32 DimArray
+  2  191×20×28 DimArray
+  3  191×20×31 DimArray
+  ⋮
+ 11  191×20×30 DimArray
+ 12  191×20×31 DimArray

And take the mean:

julia
julia> groupmeans = mean.(groups) # Take the monthly mean
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+└───────────────────────────────────────────────────────────────────────┘
+  1  0.500064
+  2  0.499762
+  3  0.500083
+  4  0.499985
+  ⋮
+ 10  0.500874
+ 11  0.498704
+ 12  0.50047

Calculate daily anomalies from the monthly mean. Notice we map a broadcast .- rather than -. This is because the size of the arrays to not match after application of mean.

julia
julia> map(.-, groupby(A, Ti=>month), mean.(groupby(A, Ti=>month), dims=Ti));

Or do something else with Y:

julia
julia> groupmeans = mean.(groupby(A, Ti=>month, Y=>isodd))
+╭──────────────────────────╮
+│ 12×2 DimArray{Float64,2} │
+├──────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points,
+  → Y  Sampled{Bool} [false, true] ForwardOrdered Irregular Points
+├────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => (:Ti=>month, :Y=>isodd)
+└────────────────────────────────────────────────────────────────────────┘
+  ↓ →  false         true
+  1        0.499594     0.500533
+  2        0.498145     0.501379
+  ⋮
+ 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.

Keywords

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.

Keywords

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

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.

Keywords

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.

Keywords

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
+
+set(dim::Dimension, index::AbstractArray) => Dimension
+set(dim::Dimension, lookup::Lookup) => Dimension
+set(dim::Dimension, lookupcomponent::LookupTrait) => Dimension
+set(dim::Dimension, metadata::AbstractMetadata) => Dimension

Set the properties of an object, its internal data or the traits of its dimensions and lookup index.

As DimensionalData is so strongly typed you do not need to specify what field of a Lookup to set - there is no ambiguity.

To set fields of a Lookup you need to specify the dimension. This can be done using X => val pairs, X = val keyword arguments, or X(val) wrapped arguments.

You can also set the fields of all dimensions by simply passing a single Lookup or lookup trait - it will be set for all dimensions.

When a Dimension or Lookup is passed to set to replace the existing ones, fields that are not set will keep their original values.

Notes:

Changing a lookup index range/vector will also update the step size and order where applicable.

Setting the Order like ForwardOrdered will not reverse the array or dimension to match. Use reverse and reorder to do this.

Examples

julia
julia> using DimensionalData; const DD = DimensionalData;
+
+julia> da = DimArray(zeros(3, 4), (custom=10.0:010.0:30.0, Z=-20:010.0:10.0));
+
+julia> set(da, ones(3, 4))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -20.0  -10.0  0.0  10.0
+ 10.0    1.0    1.0  1.0   1.0
+ 20.0    1.0    1.0  1.0   1.0
+ 30.0    1.0    1.0  1.0   1.0

Change the Dimension wrapper type:

julia
julia> set(da, :Z => Ti, :custom => Z)
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Z  Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Ti Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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

Change the lookup Vector:

julia
julia> set(da, Z => [:a, :b, :c, :d], :custom => [4, 5, 6])
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Int64} [4, 5, 6] ForwardOrdered Regular Points,
+  → Z      Sampled{Symbol} [:a, :b, :c, :d] ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+ ↓ →   :a   :b   :c   :d
+ 4    0.0  0.0  0.0  0.0
+ 5    0.0  0.0  0.0  0.0
+ 6    0.0  0.0  0.0  0.0

Change the Lookup type:

julia
julia> set(da, Z=DD.NoLookup(), custom=DD.Sampled())
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z
+└─────────────────────────────────────────────────────────────────────────┘
+ 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

Change the Sampling trait:

julia
julia> set(da, :custom => DD.Irregular(10, 12), Z => DD.Regular(9.9))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Irregular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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:

AbstractDimArray:

AbstractDimStack:

Dimension:

Lookup:

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

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))
+
+@d da1 .* da2
+@d da1 .* da2 .+ 5 dims=(Y, X)
+@d da1 .* da2 .+ 5 (dims=(Y, X), strict=false, name=:testname)

Use with @.

@d does not imply @.. You need to specify each broadcast. But @. can be used with @d as the inner macro.

julia
using DimensionalData
+da1 = ones(X(3))
+da2 = fill(2, Y(4), X(3))
+
+@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

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

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,
+→ Y  10:10:100,
+↗ Ti [0, 3, 4])
+
+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...)
+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
+
+# Create a DimArray
+da = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(300:-100:100)))
+
+# Reverse it
+rev = reverse(da, dims=Y)
+
+# using \`da\` in reorder will return it to the original order
+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

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))
+       ));
+
+julia> slices = eachslice(ds; dims=(Z, X));
+
+julia> size(slices)
+(4, 2)
+
+julia> map(dims, axes(slices))
+(↓ Z Base.OneTo(4),
+→ X Base.OneTo(2))
+
+julia> first(slices)
+╭──────────────╮
+│ 3×5 DimStack │
+├──────────────┴─────────────────────────────────── dims ┐
+  ↓ Y  Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  → Ti
+├──────────────────────────────────────────────── 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
+
+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
+
+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:

Optional keys:

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:

Optional keys:

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:

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


`,128)]))}const g=i(t,[["render",e]]);export{E as __pageData,g as default}; diff --git a/previews/PR801/assets/api_reference.md.CQevsPbp.lean.js b/previews/PR801/assets/api_reference.md.CQevsPbp.lean.js new file mode 100644 index 000000000..e9903d3c9 --- /dev/null +++ b/previews/PR801/assets/api_reference.md.CQevsPbp.lean.js @@ -0,0 +1,463 @@ +import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.D3Ia4FEQ.js";const E=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api/reference.md","filePath":"api/reference.md","lastUpdated":null}'),t={name:"api/reference.md"};function e(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`

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

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
+
+julia> ti = Ti(DateTime(2001):Month(1):DateTime(2001,12));
+
+julia> x = X(10:10:100);
+
+julia> A = DimArray(rand(12,10), (ti, x), name="example");
+
+julia> A[X(Near([12, 35])), Ti(At(DateTime(2001,5)))]
+╭───────────────────────────────────────╮
+│ 2-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴─────────────── dims ┐
+  ↓ X Sampled{Int64} [10, 40] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────┘
+ 10  0.253849
+ 40  0.637077
+
+julia> A[Near(DateTime(2001, 5, 4)), Between(20, 50)]
+╭───────────────────────────────────────╮
+│ 4-element DimArray{Float64,1} example │
+├───────────────────────────────────────┴───────────── dims ┐
+  ↓ X Sampled{Int64} 20:10:50 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────┘
+ 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
+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:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData, Random; Random.seed!(123);
+
+julia> rand(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 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:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> rand(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 1  0  0  1
+
+julia> rand(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+Base.zeros(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray

Create a DimArray of zeros.

There are two kinds of Dimension value acepted:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> zeros(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 0  0  0  0
+ 0  0  0  0
+
+julia> zeros(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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
+Base.ones(x, dims::Tuple{Vararg{Dimension}}; kw...) => DimArray

Create a DimArray of ones.

There are two kinds of Dimension value acepted:

Keywords are the same as for DimArray.

Example

julia
julia> using DimensionalData
+
+julia> ones(Bool, X(2), Y(4))
+╭──────────────────────╮
+│ 2×4 DimArray{Bool,2} │
+├──────────────── dims ┤
+  ↓ X, → Y
+└──────────────────────┘
+ 1  1  1  1
+ 1  1  1  1
+
+julia> ones(X([:a, :b, :c]), Y(100.0:50:200.0))
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
+  → Y Sampled{Float64} 100.0:50.0:200.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ ↓ →  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}}
+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

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(2, 3, 2), (X, Y, Z))
+╭───────────────────────────╮
+│ 2×3×2 DimArray{Float64,3} │
+├───────────────────── dims ┤
+  ↓ X, → Y, ↗ Z
+└───────────────────────────┘
+[:, :, 1]
+ 1.0  1.0  1.0
+ 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)
+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
+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

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));
+
+julia> otherdims(A, X)
+(↓ Y, → Z)
+
+julia> otherdims(A, (Y, Z))
+(↓ 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

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));
+
+julia> dimnum(A, (Z, X, Y))
+(3, 1, 2)
+
+julia> dimnum(A, Y)
+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

Check if an object or tuple contains an Dimension, or a tuple of dimensions.

Example

julia
julia> using DimensionalData
+
+julia> A = DimArray(ones(10, 10, 10), (X, Y, Z));
+
+julia> hasdim(A, X)
+true
+
+julia> hasdim(A, (Z, X, Y))
+(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:

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...)
+DimStack(data::NamedTuple{Keys,Vararg{AbstractDimArray}}; kw...)
+DimStack(data::NamedTuple, dims::DimTuple; metadata=NoMetadata(); kw...)

DimStack holds multiple objects sharing some dimensions, in a NamedTuple.

Notably, their behaviour lies somewhere between a DimArray and a NamedTuple:

julia
function DimStack(A::AbstractDimArray;
+    layersfrom=nothing, name=nothing, metadata=metadata(A), refdims=refdims(A), kw...
+)

For example, here we take the mean over the time dimension for all layers:

julia
mean(mydimstack; dims=Ti)

And this equivalent to:

julia
map(A -> mean(A; dims=Ti), mydimstack)

This design gives 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.

DimStack can be constructed from multiple AbstractDimArray or a NamedTuple of AbstractArray and a matching dims tuple.

Most Base and Statistics methods that apply to AbstractArray can be used on all layers of the stack simulataneously. The result is a DimStack, or a NamedTuple if methods like mean are used without dims arguments, and return a single non-array value.

Example

julia
julia> using DimensionalData
+
+julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0];
+
+julia> dimz = (X([:a, :b]), Y(10.0:10.0:30.0))
+(↓ X [:a, :b],
+→ Y 10.0:10.0:30.0)
+
+julia> da1 = DimArray(1A, dimz; name=:one);
+
+julia> da2 = DimArray(2A, dimz; name=:two);
+
+julia> da3 = DimArray(3A, dimz; name=:three);
+
+julia> s = DimStack(da1, da2, da3);
+
+julia> s[At(:b), At(10.0)]
+(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
+
+DimIndices(x)
+DimIndices(dims::Tuple)
+DimIndices(dims::Dimension)

Like CartesianIndices, but for Dimensions. Behaves as an Array of Tuple of Dimension(i) for all combinations of the axis indices of dims.

This can be used to view/index into arbitrary dimensions over an array, and is especially useful when combined with otherdims, to iterate over the indices of unknown dimension.

DimIndices can be used directly in getindex like CartesianIndices, and freely mixed with individual Dimensions or tuples of Dimension.

Example

Index a DimArray with DimIndices.

Notice that unlike CartesianIndices, it doesn't matter if the dimensions are not in the same order. Or even if they are not all contained in each.

julia
julia> A = rand(Y(0.0:0.3:1.0), X('a':'f'))
+╭─────────────────────────╮
+│ 4×6 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.3:0.9 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':1:'f' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'b'       'c'        'd'        'e'       'f'
+ 0.0  0.9063    0.253849  0.0991336  0.0320967  0.774092  0.893537
+ 0.3  0.443494  0.334152  0.125287   0.350546   0.183555  0.354868
+ 0.6  0.745673  0.427328  0.692209   0.930332   0.297023  0.131798
+ 0.9  0.512083  0.867547  0.136551   0.959434   0.150155  0.941133
+
+julia> di = DimIndices((X(1:2:4), Y(1:2:4)))
+╭─────────────────────────────────────────────╮
+│ 2×2 DimIndices{Tuple{X{Int64}, Y{Int64}},2} │
+├─────────────────────────────────────── dims ┤
+  ↓ X 1:2:3,
+  → Y 1:2:3
+└─────────────────────────────────────────────┘
+ ↓ →  1                3
+ 1     (↓ X 1, → Y 1)   (↓ X 1, → Y 3)
+ 3     (↓ X 3, → Y 1)   (↓ X 3, → Y 3)
+
+julia> A[di] # Index A with these indices
+╭─────────────────────────╮
+│ 2×2 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 0.0:0.6:0.6 ForwardOrdered Regular Points,
+  → X Categorical{Char} 'a':2:'c' ForwardOrdered
+└─────────────────────────────────────────────────────────────────┘
+ ↓ →   'a'       'c'
+ 0.0  0.9063    0.0991336
+ 0.6  0.745673  0.692209

source


# DimensionalData.DimSelectors — Type.
julia
DimSelectors <: AbstractArray
+
+DimSelectors(x; selectors, atol...)
+DimSelectors(dims::Tuple; selectors, atol...)
+DimSelectors(dims::Dimension; selectors, atol...)

Like DimIndices, but returns Dimensions holding the chosen Selectors.

Indexing into another AbstractDimArray with DimSelectors is similar to doing an interpolation.

Keywords

Example

Here we can interpolate a DimArray to the lookups of another DimArray using DimSelectors with Near. This is essentially equivalent to nearest neighbour interpolation.

julia
julia> A = rand(X(1.0:3.0:30.0), Y(1.0:5.0:30.0), Ti(1:2));
+
+julia> target = rand(X(1.0:10.0:30.0), Y(1.0:10.0:30.0));
+
+julia> A[DimSelectors(target; selectors=Near), Ti=2]
+╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} [1.0, 10.0, 22.0] ForwardOrdered Irregular Points,
+  → Y Sampled{Float64} [1.0, 11.0, 21.0] ForwardOrdered Irregular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  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
+
+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

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)
+DimTable(xs::Vararg{AbstractDimArray}; layernames=nothing, mergedims=nothing)

Construct a Tables.jl/TableTraits.jl compatible object out of an AbstractDimArray or AbstractDimStack.

This table will have columns for the array data and columns for each Dimension index, as a [DimColumn]. These are lazy, and generated as required.

Column names are converted from the dimension types using DimensionalData.name. This means type Ti becomes the column name :Ti, and Dim{:custom} becomes :custom.

To get dimension columns, you can index with Dimension (X()) or Dimension type (X) as well as the regular Int or Symbol.

Keywords

Example

julia
julia> using DimensionalData, Tables
+
+julia> a = DimArray(ones(16, 16, 3), (X, Y, Dim{:band}))
+╭─────────────────────────────╮
+│ 16×16×3 DimArray{Float64,3} │
+├─────────────────────── dims ┤
+  ↓ X, → Y, ↗ band
+└─────────────────────────────┘
+[:, :, 1]
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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...)
+groupby(A::Union{AbstractDimArray,AbstractDimStack}, dims::Dimension{<:Callable}...)

Group A by grouping functions or Bins over multiple dimensions.

Arguments

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)));
+
+julia> groups = groupby(A, Ti => month) # Group by month
+╭───────────────────────────────────────────────────╮
+│ 12-element DimGroupByArray{DimArray{Float64,2},1} │
+├───────────────────────────────────────────────────┴───────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+├─────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Y, ↗ Ti
+└───────────────────────────────────────────────────────────────────────┘
+  1  191×20×32 DimArray
+  2  191×20×28 DimArray
+  3  191×20×31 DimArray
+  ⋮
+ 11  191×20×30 DimArray
+ 12  191×20×31 DimArray

And take the mean:

julia
julia> groupmeans = mean.(groups) # Take the monthly mean
+╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+└───────────────────────────────────────────────────────────────────────┘
+  1  0.500064
+  2  0.499762
+  3  0.500083
+  4  0.499985
+  ⋮
+ 10  0.500874
+ 11  0.498704
+ 12  0.50047

Calculate daily anomalies from the monthly mean. Notice we map a broadcast .- rather than -. This is because the size of the arrays to not match after application of mean.

julia
julia> map(.-, groupby(A, Ti=>month), mean.(groupby(A, Ti=>month), dims=Ti));

Or do something else with Y:

julia
julia> groupmeans = mean.(groupby(A, Ti=>month, Y=>isodd))
+╭──────────────────────────╮
+│ 12×2 DimArray{Float64,2} │
+├──────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points,
+  → Y  Sampled{Bool} [false, true] ForwardOrdered Irregular Points
+├────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => (:Ti=>month, :Y=>isodd)
+└────────────────────────────────────────────────────────────────────────┘
+  ↓ →  false         true
+  1        0.499594     0.500533
+  2        0.498145     0.501379
+  ⋮
+ 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.

Keywords

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.

Keywords

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

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.

Keywords

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.

Keywords

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
+
+set(dim::Dimension, index::AbstractArray) => Dimension
+set(dim::Dimension, lookup::Lookup) => Dimension
+set(dim::Dimension, lookupcomponent::LookupTrait) => Dimension
+set(dim::Dimension, metadata::AbstractMetadata) => Dimension

Set the properties of an object, its internal data or the traits of its dimensions and lookup index.

As DimensionalData is so strongly typed you do not need to specify what field of a Lookup to set - there is no ambiguity.

To set fields of a Lookup you need to specify the dimension. This can be done using X => val pairs, X = val keyword arguments, or X(val) wrapped arguments.

You can also set the fields of all dimensions by simply passing a single Lookup or lookup trait - it will be set for all dimensions.

When a Dimension or Lookup is passed to set to replace the existing ones, fields that are not set will keep their original values.

Notes:

Changing a lookup index range/vector will also update the step size and order where applicable.

Setting the Order like ForwardOrdered will not reverse the array or dimension to match. Use reverse and reorder to do this.

Examples

julia
julia> using DimensionalData; const DD = DimensionalData;
+
+julia> da = DimArray(zeros(3, 4), (custom=10.0:010.0:30.0, Z=-20:010.0:10.0));
+
+julia> set(da, ones(3, 4))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -20.0  -10.0  0.0  10.0
+ 10.0    1.0    1.0  1.0   1.0
+ 20.0    1.0    1.0  1.0   1.0
+ 30.0    1.0    1.0  1.0   1.0

Change the Dimension wrapper type:

julia
julia> set(da, :Z => Ti, :custom => Z)
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Z  Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Ti Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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

Change the lookup Vector:

julia
julia> set(da, Z => [:a, :b, :c, :d], :custom => [4, 5, 6])
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Int64} [4, 5, 6] ForwardOrdered Regular Points,
+  → Z      Sampled{Symbol} [:a, :b, :c, :d] ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────────┘
+ ↓ →   :a   :b   :c   :d
+ 4    0.0  0.0  0.0  0.0
+ 5    0.0  0.0  0.0  0.0
+ 6    0.0  0.0  0.0  0.0

Change the Lookup type:

julia
julia> set(da, Z=DD.NoLookup(), custom=DD.Sampled())
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Regular Points,
+  → Z
+└─────────────────────────────────────────────────────────────────────────┘
+ 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

Change the Sampling trait:

julia
julia> set(da, :custom => DD.Irregular(10, 12), Z => DD.Regular(9.9))
+╭─────────────────────────╮
+│ 3×4 DimArray{Float64,2} │
+├─────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ custom Sampled{Float64} 10.0:10.0:30.0 ForwardOrdered Irregular Points,
+  → Z      Sampled{Float64} -20.0:10.0:10.0 ForwardOrdered Regular Points
+└───────────────────────────────────────────────────────────────────────────┘
+  ↓ →  -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:

AbstractDimArray:

AbstractDimStack:

Dimension:

Lookup:

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

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))
+
+@d da1 .* da2
+@d da1 .* da2 .+ 5 dims=(Y, X)
+@d da1 .* da2 .+ 5 (dims=(Y, X), strict=false, name=:testname)

Use with @.

@d does not imply @.. You need to specify each broadcast. But @. can be used with @d as the inner macro.

julia
using DimensionalData
+da1 = ones(X(3))
+da2 = fill(2, Y(4), X(3))
+
+@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

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

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,
+→ Y  10:10:100,
+↗ Ti [0, 3, 4])
+
+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...)
+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
+
+# Create a DimArray
+da = DimArray([1 2 3; 4 5 6], (X(10:10:20), Y(300:-100:100)))
+
+# Reverse it
+rev = reverse(da, dims=Y)
+
+# using \`da\` in reorder will return it to the original order
+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

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))
+       ));
+
+julia> slices = eachslice(ds; dims=(Z, X));
+
+julia> size(slices)
+(4, 2)
+
+julia> map(dims, axes(slices))
+(↓ Z Base.OneTo(4),
+→ X Base.OneTo(2))
+
+julia> first(slices)
+╭──────────────╮
+│ 3×5 DimStack │
+├──────────────┴─────────────────────────────────── dims ┐
+  ↓ Y  Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  → Ti
+├──────────────────────────────────────────────── 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
+
+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
+
+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:

Optional keys:

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:

Optional keys:

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:

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


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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))
╭─────────────────────────╮
+│ 4×5 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ 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',))
╭─────────────────────────────╮
+│ 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}:
+[:, :, 1] =
+  65  110   71  108  76  15  120   65  121  126
+ 125   78  118  101  11  32   87  108   49  115
+
+[:, :, 2] =
+ 106  84  74  80   9   26  35  74  28  47
+  87   1  42  54  15  107  77  23  89  34
+
+[:, :, 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))
╭─────────────────────────╮
+│ 2×10×3 DimArray{Int8,3} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
+  → time    Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ 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}; diff --git a/previews/PR801/assets/basics.md.LP9Fcdo1.lean.js b/previews/PR801/assets/basics.md.LP9Fcdo1.lean.js new file mode 100644 index 000000000..da9046332 --- /dev/null +++ b/previews/PR801/assets/basics.md.LP9Fcdo1.lean.js @@ -0,0 +1,49 @@ +import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.D3Ia4FEQ.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))
╭─────────────────────────╮
+│ 4×5 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ 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',))
╭─────────────────────────────╮
+│ 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}:
+[:, :, 1] =
+  65  110   71  108  76  15  120   65  121  126
+ 125   78  118  101  11  32   87  108   49  115
+
+[:, :, 2] =
+ 106  84  74  80   9   26  35  74  28  47
+  87   1  42  54  15  107  77  23  89  34
+
+[:, :, 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))
╭─────────────────────────╮
+│ 2×10×3 DimArray{Int8,3} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
+  → time    Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ 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}; diff --git a/previews/PR801/assets/broadcasts.md.5J_tBPs5.js b/previews/PR801/assets/broadcasts.md.5J_tBPs5.js new file mode 100644 index 000000000..827a22857 --- /dev/null +++ b/previews/PR801/assets/broadcasts.md.5J_tBPs5.js @@ -0,0 +1,138 @@ +import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.D3Ia4FEQ.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
+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,
+↗ Ti Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00"))

A DimArray from 1:12 to scale with:

julia
julia> month_scalars = DimArray(month, t)
╭────────────────────────────────────────╮
+│ 12-element DimArray{Int64,1} month(Ti) │
+├────────────────────────────────────────┴─────────────────────────────── 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   1
+ 2000-02-01T00:00:00   2
+ 2000-03-01T00:00:00   3
+ 2000-04-01T00:00:00   4
+ 2000-05-01T00:00:00   5
+ 2000-06-01T00:00:00   6
+ 2000-07-01T00:00:00   7
+ 2000-08-01T00:00:00   8
+ 2000-09-01T00:00:00   9
+ 2000-10-01T00:00:00  10
+ 2000-11-01T00:00:00  11
+ 2000-12-01T00:00:00  12

And a larger DimArray for example data:

julia
julia> data = rand(x, y, t)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)))
╭────────────────────────────────╮
+│ 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)))
╭────────────────────────────────╮
+│ 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)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)
╭───────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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)
╭──────────────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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}; diff --git a/previews/PR801/assets/broadcasts.md.5J_tBPs5.lean.js b/previews/PR801/assets/broadcasts.md.5J_tBPs5.lean.js new file mode 100644 index 000000000..827a22857 --- /dev/null +++ b/previews/PR801/assets/broadcasts.md.5J_tBPs5.lean.js @@ -0,0 +1,138 @@ +import{_ as i,c as a,a4 as n,o as e}from"./chunks/framework.D3Ia4FEQ.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
+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,
+↗ Ti Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00"))

A DimArray from 1:12 to scale with:

julia
julia> month_scalars = DimArray(month, t)
╭────────────────────────────────────────╮
+│ 12-element DimArray{Int64,1} month(Ti) │
+├────────────────────────────────────────┴─────────────────────────────── 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   1
+ 2000-02-01T00:00:00   2
+ 2000-03-01T00:00:00   3
+ 2000-04-01T00:00:00   4
+ 2000-05-01T00:00:00   5
+ 2000-06-01T00:00:00   6
+ 2000-07-01T00:00:00   7
+ 2000-08-01T00:00:00   8
+ 2000-09-01T00:00:00   9
+ 2000-10-01T00:00:00  10
+ 2000-11-01T00:00:00  11
+ 2000-12-01T00:00:00  12

And a larger DimArray for example data:

julia
julia> data = rand(x, y, t)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)))
╭────────────────────────────────╮
+│ 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)))
╭────────────────────────────────╮
+│ 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)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)
╭───────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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)
╭──────────────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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
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e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=We,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Te(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Be.batchSize,r=e.batchWait||Be.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Pe.minDirtCount,s=s||Pe.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===le.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Te(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Ee(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new le(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:De(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Je){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const 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 c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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GPUs","description":"","frontmatter":{},"headers":[],"relativePath":"cuda.md","filePath":"cuda.md","lastUpdated":null}'),t={name:"cuda.md"};function l(k,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`

CUDA & GPUs ​

Running regular julia code on GPUs is one of the most amazing things about the language. DimensionalData.jl leans into this as much as possible.

julia
using DimensionalData, CUDA
+
+# Create a Float32 array to use on the GPU
+A = rand(Float32, X(1.0:1000.0), Y(1.0:2000.0))
+
+# Move the parent data to the GPU with \`modify\` and the \`CuArray\` constructor:
+cuA = modify(CuArray, A)

The result of a GPU broadcast is still a DimArray:

julia
julia> cuA2 = cuA .* 2
+╭───────────────────────────────╮
+│ 1000×2000 DimArray{Float32,2} │
+├───────────────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:1000.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 1.0:1.0:2000.0 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────────────┘
+    ↓ →  1.0       2.0        3.0        4.0       …  1998.0        1999.0        2000.0
+    1.0  1.69506   1.28405    0.989952   0.900394        1.73623       1.30427       1.98193
+    2.0  1.73591   0.929995   0.665742   0.345501        0.162919      1.81708       0.702944
+    3.0  1.24575   1.80455    1.78028    1.49097         0.45804       0.224375      0.0197492
+    4.0  0.374026  1.91495    1.17645    0.995683        0.835288      1.54822       0.487601
+    5.0  1.17673   0.0557598  0.183637   1.90645   …     0.88058       1.23788       1.59705
+    6.0  1.57019   0.215049   1.9155     0.982762        0.906838      0.1076        0.390081
+    ⋮                                              ⋱                              
+  995.0  1.48275   0.40409    1.37963    1.66622         0.462981      1.4492        1.26917
+  996.0  1.88869   1.86174    0.298383   0.854739  …     0.778222      1.42151       1.75568
+  997.0  1.88092   1.87436    0.285965   0.304688        1.32669       0.0599431     0.134186
+  998.0  1.18035   1.61025    0.352614   1.75847         0.464554      1.90309       1.30923
+  999.0  1.40584   1.83056    0.0804518  0.177423        1.20779       1.95217       0.881149
+ 1000.0  1.41334   0.719974   0.479126   1.92721         0.0649391     0.642908      1.07277

But the data is on the GPU:

julia
julia> typeof(parent(cuA2))
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

GPU Integration goals ​

DimensionalData.jl has two GPU-related goals:

  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}; diff --git a/previews/PR801/assets/cuda.md.Rut_ExXL.lean.js b/previews/PR801/assets/cuda.md.Rut_ExXL.lean.js new file mode 100644 index 000000000..103555356 --- /dev/null +++ b/previews/PR801/assets/cuda.md.Rut_ExXL.lean.js @@ -0,0 +1,28 @@ +import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.D3Ia4FEQ.js";const C=JSON.parse('{"title":"CUDA & GPUs","description":"","frontmatter":{},"headers":[],"relativePath":"cuda.md","filePath":"cuda.md","lastUpdated":null}'),t={name:"cuda.md"};function l(k,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`

CUDA & GPUs ​

Running regular julia code on GPUs is one of the most amazing things about the language. DimensionalData.jl leans into this as much as possible.

julia
using DimensionalData, CUDA
+
+# Create a Float32 array to use on the GPU
+A = rand(Float32, X(1.0:1000.0), Y(1.0:2000.0))
+
+# Move the parent data to the GPU with \`modify\` and the \`CuArray\` constructor:
+cuA = modify(CuArray, A)

The result of a GPU broadcast is still a DimArray:

julia
julia> cuA2 = cuA .* 2
+╭───────────────────────────────╮
+│ 1000×2000 DimArray{Float32,2} │
+├───────────────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:1000.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 1.0:1.0:2000.0 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────────────┘
+    ↓ →  1.0       2.0        3.0        4.0       …  1998.0        1999.0        2000.0
+    1.0  1.69506   1.28405    0.989952   0.900394        1.73623       1.30427       1.98193
+    2.0  1.73591   0.929995   0.665742   0.345501        0.162919      1.81708       0.702944
+    3.0  1.24575   1.80455    1.78028    1.49097         0.45804       0.224375      0.0197492
+    4.0  0.374026  1.91495    1.17645    0.995683        0.835288      1.54822       0.487601
+    5.0  1.17673   0.0557598  0.183637   1.90645   …     0.88058       1.23788       1.59705
+    6.0  1.57019   0.215049   1.9155     0.982762        0.906838      0.1076        0.390081
+    ⋮                                              ⋱                              
+  995.0  1.48275   0.40409    1.37963    1.66622         0.462981      1.4492        1.26917
+  996.0  1.88869   1.86174    0.298383   0.854739  …     0.778222      1.42151       1.75568
+  997.0  1.88092   1.87436    0.285965   0.304688        1.32669       0.0599431     0.134186
+  998.0  1.18035   1.61025    0.352614   1.75847         0.464554      1.90309       1.30923
+  999.0  1.40584   1.83056    0.0804518  0.177423        1.20779       1.95217       0.881149
+ 1000.0  1.41334   0.719974   0.479126   1.92721         0.0649391     0.642908      1.07277

But the data is on the GPU:

julia
julia> typeof(parent(cuA2))
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

GPU Integration goals ​

DimensionalData.jl has two GPU-related goals:

  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}; diff --git a/previews/PR801/assets/dimarrays.md.7pjFj3JZ.js b/previews/PR801/assets/dimarrays.md.7pjFj3JZ.js new file mode 100644 index 000000000..73f257d09 --- /dev/null +++ b/previews/PR801/assets/dimarrays.md.7pjFj3JZ.js @@ -0,0 +1,259 @@ +import{_ as d,B as l,c as p,a4 as k,G as h,w as e,o as g,j as i,a as s}from"./chunks/framework.D3Ia4FEQ.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=l("PluginTabsTab"),n=l("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=k(`

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))
╭──────────────────────────╮
+│ 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"}},", 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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"}},"); name"),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"}},", metadata"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"Dict"),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 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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," ↓ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"→ "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"Y")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"├────────────────────────────────────────────────────── metadata ┤")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Dict{Any, Any}()")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└────────────────────────────────────────────────────────────────┘")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")])])])],-1)])),_:1})]),_:1}),a[6]||(a[6]=k(`

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]
╭───────────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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))]
╭───────────────────────────────╮
+│ 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}}}:
+ (↓ X 3, → Z 5)
+ (↓ X 7, → Z 4)
+ (↓ X 8, → Z 2)
julia
julia> da2[inds]
╭─────────────────────────╮
+│ 7×3 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ Y ,
+  → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
+└──────────────────────────────────────────────────────────────────────────────┘
+  (3, 5)     (7, 4)    (8, 2)
+ 0.794017   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)]
╭──────────────────────────╮
+│ 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.
+└ @ 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.
+└ @ 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))
╭───────────────────────────╮
+│ 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)
╭───────────────────────────╮
+│ 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
+
+julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ X, → Y
+└────────────────────────────────────────────────────────────────┘
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
julia
julia> @benchmark $da4[X(1), Y(2)]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range (min … max):  3.086 ns … 17.533 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.105 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.110 ns ±  0.232 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+               ▅█            ▅█              ▂             ▂ ▂
+  ▃▁▁▁▁▁▁▁▁▁▁▁▁██▆▁▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▁▁▁██▅▁▁▁▁▁▁▁▁▁▁▁██ █
+  3.09 ns      Histogram: log(frequency) by time     3.13 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 … 19.307 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.096 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.112 ns ±  0.329 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+   █                  ▃                                      
+  ▅█▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
+  3.1 ns         Histogram: frequency by time        3.13 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/previews/PR801/assets/dimarrays.md.7pjFj3JZ.lean.js b/previews/PR801/assets/dimarrays.md.7pjFj3JZ.lean.js new file mode 100644 index 000000000..73f257d09 --- /dev/null +++ b/previews/PR801/assets/dimarrays.md.7pjFj3JZ.lean.js @@ -0,0 +1,259 @@ +import{_ as d,B as l,c as p,a4 as k,G as h,w as e,o as g,j as i,a as s}from"./chunks/framework.D3Ia4FEQ.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=l("PluginTabsTab"),n=l("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=k(`

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))
╭──────────────────────────╮
+│ 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:

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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]
╭───────────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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))]
╭───────────────────────────────╮
+│ 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}}}:
+ (↓ X 3, → Z 5)
+ (↓ X 7, → Z 4)
+ (↓ X 8, → Z 2)
julia
julia> da2[inds]
╭─────────────────────────╮
+│ 7×3 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ Y ,
+  → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
+└──────────────────────────────────────────────────────────────────────────────┘
+  (3, 5)     (7, 4)    (8, 2)
+ 0.794017   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)]
╭──────────────────────────╮
+│ 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.
+└ @ 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.
+└ @ 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))
╭───────────────────────────╮
+│ 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)
╭───────────────────────────╮
+│ 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
+
+julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ X, → Y
+└────────────────────────────────────────────────────────────────┘
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
julia
julia> @benchmark $da4[X(1), Y(2)]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range (min … max):  3.086 ns … 17.533 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.105 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.110 ns ±  0.232 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+               ▅█            ▅█              ▂             ▂ ▂
+  ▃▁▁▁▁▁▁▁▁▁▁▁▁██▆▁▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▁▁▁██▅▁▁▁▁▁▁▁▁▁▁▁██ █
+  3.09 ns      Histogram: log(frequency) by time     3.13 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 … 19.307 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.096 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.112 ns ±  0.329 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+   █                  ▃                                      
+  ▅█▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
+  3.1 ns         Histogram: frequency by time        3.13 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/previews/PR801/assets/dimensions.md.CBs16yaQ.js b/previews/PR801/assets/dimensions.md.CBs16yaQ.js new file mode 100644 index 000000000..b1c707377 --- /dev/null +++ b/previews/PR801/assets/dimensions.md.CBs16yaQ.js @@ -0,0 +1,3 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.D3Ia4FEQ.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/previews/PR801/assets/dimensions.md.CBs16yaQ.lean.js b/previews/PR801/assets/dimensions.md.CBs16yaQ.lean.js new file mode 100644 index 000000000..b1c707377 --- /dev/null +++ b/previews/PR801/assets/dimensions.md.CBs16yaQ.lean.js @@ -0,0 +1,3 @@ +import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.D3Ia4FEQ.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/previews/PR801/assets/diskarrays.md.64JEAR8Z.js b/previews/PR801/assets/diskarrays.md.64JEAR8Z.js new file mode 100644 index 000000000..1e18b80b5 --- /dev/null +++ b/previews/PR801/assets/diskarrays.md.64JEAR8Z.js @@ -0,0 +1 @@ +import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.D3Ia4FEQ.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}; diff --git a/previews/PR801/assets/diskarrays.md.64JEAR8Z.lean.js b/previews/PR801/assets/diskarrays.md.64JEAR8Z.lean.js new file mode 100644 index 000000000..1e18b80b5 --- /dev/null +++ b/previews/PR801/assets/diskarrays.md.64JEAR8Z.lean.js @@ -0,0 +1 @@ +import{_ as t,c as i,a4 as r,o as e}from"./chunks/framework.D3Ia4FEQ.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}; diff --git a/previews/PR801/assets/extending_dd.md.DvBZFGy6.js b/previews/PR801/assets/extending_dd.md.DvBZFGy6.js new file mode 100644 index 000000000..cfd94be64 --- /dev/null +++ b/previews/PR801/assets/extending_dd.md.DvBZFGy6.js @@ -0,0 +1,55 @@ +import{_ as n,B as l,c as d,a4 as r,G as a,w as t,o as p,j as i,a as s}from"./chunks/framework.D3Ia4FEQ.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 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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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)
╭───────────────────────────╮
+│ 10×11 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
+  → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  100.0         110.0        …  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- 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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
+using DimensionalData.Lookups
+x, y = X(10:-1:1), Y(100.0:10:200.0)
(↓ X 10:-1:1,
+→ Y 100.0:10.0:200.0)
julia
julia> A = rand(x, y)
╭───────────────────────────╮
+│ 10×11 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
+  → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  100.0         110.0        …  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- 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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
+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

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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))
╭───────────────────────────────╮
+│ 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
+ ⋮                                                 ⋱  ⋮
+ 1.96  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
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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
+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

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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))
╭───────────────────────────────╮
+│ 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
+ ⋮                                                 ⋱  ⋮
+ 1.96  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
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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/previews/PR801/assets/integrations.md.DnteQbzv.lean.js b/previews/PR801/assets/integrations.md.DnteQbzv.lean.js new file mode 100644 index 000000000..af66c3b8c --- /dev/null +++ b/previews/PR801/assets/integrations.md.DnteQbzv.lean.js @@ -0,0 +1 @@ +import{_ as e,c as r,a4 as t,o}from"./chunks/framework.D3Ia4FEQ.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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objects","description":"","frontmatter":{},"headers":[],"relativePath":"object_modification.md","filePath":"object_modification.md","lastUpdated":null}'),g={name:"object_modification.md"};function y(o,a,c,f,u,E){const h=n("PluginTabsTab"),t=n("PluginTabs");return r(),p("div",null,[a[12]||(a[12]=k('

Modifying objects ​

DimensionalData.jl objects are all struct rather than mutable struct. 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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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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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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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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0.340557")])])])],-1),i("p",null,":::",-1)])),_:1})]),_:1})])}const v=d(g,[["render",y]]);export{m as __pageData,v as default}; diff --git a/previews/PR801/assets/plots.md.BM84STtN.js b/previews/PR801/assets/plots.md.BM84STtN.js new file mode 100644 index 000000000..7402bba24 --- /dev/null +++ b/previews/PR801/assets/plots.md.BM84STtN.js @@ -0,0 +1,5 @@ +import{_ as i,c as a,a4 as t,o as e}from"./chunks/framework.D3Ia4FEQ.js";const l="/DimensionalData.jl/previews/PR801/assets/jmmhsxu.iiL4UBgm.png",h="/DimensionalData.jl/previews/PR801/assets/ybmzrjc.BIa4VhRY.png",n="/DimensionalData.jl/previews/PR801/assets/veptoqy.CZKDtjAN.png",p="/DimensionalData.jl/previews/PR801/assets/izkfogt.nOjqWh1g.png",k="/DimensionalData.jl/previews/PR801/assets/yhxwiti.CBNkYhiq.png",r="/DimensionalData.jl/previews/PR801/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 e(),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]))
+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!

',26)]))}const u=i(d,[["render",o]]);export{m as __pageData,u as default}; diff --git a/previews/PR801/assets/plots.md.BM84STtN.lean.js b/previews/PR801/assets/plots.md.BM84STtN.lean.js new file mode 100644 index 000000000..7402bba24 --- /dev/null +++ b/previews/PR801/assets/plots.md.BM84STtN.lean.js @@ -0,0 +1,5 @@ +import{_ as i,c as a,a4 as t,o as e}from"./chunks/framework.D3Ia4FEQ.js";const l="/DimensionalData.jl/previews/PR801/assets/jmmhsxu.iiL4UBgm.png",h="/DimensionalData.jl/previews/PR801/assets/ybmzrjc.BIa4VhRY.png",n="/DimensionalData.jl/previews/PR801/assets/veptoqy.CZKDtjAN.png",p="/DimensionalData.jl/previews/PR801/assets/izkfogt.nOjqWh1g.png",k="/DimensionalData.jl/previews/PR801/assets/yhxwiti.CBNkYhiq.png",r="/DimensionalData.jl/previews/PR801/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 e(),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]))
+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!

',26)]))}const u=i(d,[["render",o]]);export{m as __pageData,u as default}; diff --git a/previews/PR801/assets/rcywifo.NnnTWDGw.png b/previews/PR801/assets/rcywifo.NnnTWDGw.png new file mode 100644 index 000000000..3653bcd6f Binary files /dev/null and b/previews/PR801/assets/rcywifo.NnnTWDGw.png differ diff --git a/previews/PR801/assets/selectors.md.DMIZn364.js b/previews/PR801/assets/selectors.md.DMIZn364.js new file mode 100644 index 000000000..5649a443d --- /dev/null +++ b/previews/PR801/assets/selectors.md.DMIZn364.js @@ -0,0 +1,258 @@ +import{_ as p,B as n,c as d,a4 as k,G as l,w as h,o as r,j as i,a as s}from"./chunks/framework.D3Ia4FEQ.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]))
╭─────────────────────────╮
+│ 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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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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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"}},"12-element "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"DimArray{String,1}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," monthabbr(X)"),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"}}," Cyclic{Dates.DateTime} "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},'Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00")'),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"}},"└──────────────────────────────────────────────────────────────────────────────┘")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-01-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jan"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-02-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Feb"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-03-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Mar"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-04-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Apr"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-05-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "May"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-06-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jun"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-07-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jul"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-08-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Aug"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-09-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Sep"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-10-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Oct"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-11-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Nov"')]),s(` +`),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 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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 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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 show in REPL printing.")],-1),i("p",null,[s("Here we create a "),i("a",{href:"/DimensionalData.jl/previews/PR801/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))
╭─────────────────────────╮
+│ 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))
╭───────────────────────────╮
+│ 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
+ ⋮                                       ⋱               ⋮
+ 1.84   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)]
╭─────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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)]
╭─────────────────────────╮
+│ 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}; diff --git a/previews/PR801/assets/selectors.md.DMIZn364.lean.js b/previews/PR801/assets/selectors.md.DMIZn364.lean.js new file mode 100644 index 000000000..5649a443d --- /dev/null +++ b/previews/PR801/assets/selectors.md.DMIZn364.lean.js @@ -0,0 +1,258 @@ +import{_ as p,B as n,c as d,a4 as k,G as l,w as h,o as r,j as i,a as s}from"./chunks/framework.D3Ia4FEQ.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]))
╭─────────────────────────╮
+│ 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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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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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"}},"12-element "),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"DimArray{String,1}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," monthabbr(X)"),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"}}," Cyclic{Dates.DateTime} "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},'Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00")'),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"}},"└──────────────────────────────────────────────────────────────────────────────┘")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-01-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jan"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-02-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Feb"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-03-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Mar"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-04-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Apr"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-05-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "May"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-06-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jun"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-07-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Jul"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-08-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Aug"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-09-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Sep"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-10-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Oct"')]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," 2000-11-01T00:00:00"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' "Nov"')]),s(` +`),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 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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 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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 show in REPL printing.")],-1),i("p",null,[s("Here we create a "),i("a",{href:"/DimensionalData.jl/previews/PR801/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))
╭─────────────────────────╮
+│ 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))
╭───────────────────────────╮
+│ 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
+ ⋮                                       ⋱               ⋮
+ 1.84   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)]
╭─────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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)]
╭─────────────────────────╮
+│ 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}; diff --git a/previews/PR801/assets/stacks.md.C7pJh52H.js b/previews/PR801/assets/stacks.md.C7pJh52H.js new file mode 100644 index 000000000..010599cb7 --- /dev/null +++ b/previews/PR801/assets/stacks.md.C7pJh52H.js @@ -0,0 +1,368 @@ +import{_ as n,B as d,c as p,a4 as t,G as h,w as l,j as i,a as s,o as r}from"./chunks/framework.D3Ia4FEQ.js";const b=JSON.parse('{"title":"DimStacks","description":"","frontmatter":{},"headers":[],"relativePath":"stacks.md","filePath":"stacks.md","lastUpdated":null}'),g={name:"stacks.md"};function y(f,a,c,o,u,E){const k=d("PluginTabsTab"),e=d("PluginTabs");return r(),p("div",null,[a[25]||(a[25]=t(`

DimStacks ​

An AbstractDimStack represents a collection of AbstractDimArray layers that share some or all dimensions. For any two layers, a dimension of the same name must have the identical lookup - in fact only one is stored for all layers to enforce this consistency.

julia
julia> using DimensionalData
+
+julia> x, y = X(1.0:10.0), Y(5.0:10.0)
(↓ X 1.0:1.0:10.0,
+→ Y 5.0:1.0:10.0)
julia
julia> st = DimStack((a=rand(x, y), b=rand(x, y), c=rand(y), d=rand(x)))
╭───────────────╮
+│ 10×6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+  :a eltype: Float64 dims: X, Y size: 10×6
+  :b eltype: Float64 dims: X, Y size: 10×6
+  :c eltype: Float64 dims: Y size: 6
+  :d eltype: Float64 dims: X size: 10
+└──────────────────────────────────────────────────────────────────────────────┘

The behaviour of a DimStack is at times like a NamedTuple of DimArray and, others an AbstractArray of NamedTuple.

NamedTuple-like indexing ​

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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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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
+
+julia> @btime $st[X=1, Y=4]
  4.037 ns (0 allocations: 0 bytes)
+(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.027 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}; diff --git a/previews/PR801/assets/stacks.md.C7pJh52H.lean.js b/previews/PR801/assets/stacks.md.C7pJh52H.lean.js new file mode 100644 index 000000000..010599cb7 --- /dev/null +++ b/previews/PR801/assets/stacks.md.C7pJh52H.lean.js @@ -0,0 +1,368 @@ +import{_ as n,B as d,c as p,a4 as t,G as h,w as l,j as i,a as s,o as r}from"./chunks/framework.D3Ia4FEQ.js";const b=JSON.parse('{"title":"DimStacks","description":"","frontmatter":{},"headers":[],"relativePath":"stacks.md","filePath":"stacks.md","lastUpdated":null}'),g={name:"stacks.md"};function y(f,a,c,o,u,E){const k=d("PluginTabsTab"),e=d("PluginTabs");return r(),p("div",null,[a[25]||(a[25]=t(`

DimStacks ​

An AbstractDimStack represents a collection of AbstractDimArray layers that share some or all dimensions. For any two layers, a dimension of the same name must have the identical lookup - in fact only one is stored for all layers to enforce this consistency.

julia
julia> using DimensionalData
+
+julia> x, y = X(1.0:10.0), Y(5.0:10.0)
(↓ X 1.0:1.0:10.0,
+→ Y 5.0:1.0:10.0)
julia
julia> st = DimStack((a=rand(x, y), b=rand(x, y), c=rand(y), d=rand(x)))
╭───────────────╮
+│ 10×6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+  :a eltype: Float64 dims: X, Y size: 10×6
+  :b eltype: Float64 dims: X, Y size: 10×6
+  :c eltype: Float64 dims: Y size: 6
+  :d eltype: Float64 dims: X size: 10
+└──────────────────────────────────────────────────────────────────────────────┘

The behaviour of a DimStack is at times like a NamedTuple of DimArray and, others an AbstractArray of NamedTuple.

NamedTuple-like indexing ​

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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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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
+
+julia> @btime $st[X=1, Y=4]
  4.037 ns (0 allocations: 0 bytes)
+(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.027 ns (0 allocations: 0 bytes)
+(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
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var(--vp-c-black)}:root{--vp-c-gray-1: #dddde3;--vp-c-gray-2: #e4e4e9;--vp-c-gray-3: #ebebef;--vp-c-gray-soft: rgba(142, 150, 170, .14);--vp-c-indigo-1: #3451b2;--vp-c-indigo-2: #3a5ccc;--vp-c-indigo-3: #5672cd;--vp-c-indigo-soft: rgba(100, 108, 255, .14);--vp-c-purple-1: #6f42c1;--vp-c-purple-2: #7e4cc9;--vp-c-purple-3: #8e5cd9;--vp-c-purple-soft: rgba(159, 122, 234, .14);--vp-c-green-1: #18794e;--vp-c-green-2: #299764;--vp-c-green-3: #30a46c;--vp-c-green-soft: rgba(16, 185, 129, .14);--vp-c-yellow-1: #915930;--vp-c-yellow-2: #946300;--vp-c-yellow-3: #9f6a00;--vp-c-yellow-soft: rgba(234, 179, 8, .14);--vp-c-red-1: #b8272c;--vp-c-red-2: #d5393e;--vp-c-red-3: #e0575b;--vp-c-red-soft: rgba(244, 63, 94, .14);--vp-c-sponsor: #db2777}.dark{--vp-c-gray-1: #515c67;--vp-c-gray-2: #414853;--vp-c-gray-3: #32363f;--vp-c-gray-soft: rgba(101, 117, 133, .16);--vp-c-indigo-1: #a8b1ff;--vp-c-indigo-2: #5c73e7;--vp-c-indigo-3: #3e63dd;--vp-c-indigo-soft: rgba(100, 108, 255, .16);--vp-c-purple-1: #c8abfa;--vp-c-purple-2: #a879e6;--vp-c-purple-3: #8e5cd9;--vp-c-purple-soft: rgba(159, 122, 234, .16);--vp-c-green-1: #3dd68c;--vp-c-green-2: #30a46c;--vp-c-green-3: #298459;--vp-c-green-soft: rgba(16, 185, 129, .16);--vp-c-yellow-1: #f9b44e;--vp-c-yellow-2: #da8b17;--vp-c-yellow-3: #a46a0a;--vp-c-yellow-soft: rgba(234, 179, 8, .16);--vp-c-red-1: #f66f81;--vp-c-red-2: #f14158;--vp-c-red-3: #b62a3c;--vp-c-red-soft: rgba(244, 63, 94, .16)}:root{--vp-c-bg: #ffffff;--vp-c-bg-alt: #f6f6f7;--vp-c-bg-elv: #ffffff;--vp-c-bg-soft: #f6f6f7}.dark{--vp-c-bg: #1b1b1f;--vp-c-bg-alt: #161618;--vp-c-bg-elv: #202127;--vp-c-bg-soft: #202127}:root{--vp-c-border: #c2c2c4;--vp-c-divider: #e2e2e3;--vp-c-gutter: #e2e2e3}.dark{--vp-c-border: #3c3f44;--vp-c-divider: #2e2e32;--vp-c-gutter: #000000}:root{--vp-c-text-1: rgba(60, 60, 67);--vp-c-text-2: rgba(60, 60, 67, .78);--vp-c-text-3: rgba(60, 60, 67, .56)}.dark{--vp-c-text-1: rgba(255, 255, 245, .86);--vp-c-text-2: rgba(235, 235, 245, .6);--vp-c-text-3: rgba(235, 235, 245, .38)}:root{--vp-c-default-1: var(--vp-c-gray-1);--vp-c-default-2: var(--vp-c-gray-2);--vp-c-default-3: var(--vp-c-gray-3);--vp-c-default-soft: var(--vp-c-gray-soft);--vp-c-brand-1: var(--vp-c-indigo-1);--vp-c-brand-2: var(--vp-c-indigo-2);--vp-c-brand-3: var(--vp-c-indigo-3);--vp-c-brand-soft: var(--vp-c-indigo-soft);--vp-c-brand: var(--vp-c-brand-1);--vp-c-tip-1: var(--vp-c-brand-1);--vp-c-tip-2: var(--vp-c-brand-2);--vp-c-tip-3: var(--vp-c-brand-3);--vp-c-tip-soft: var(--vp-c-brand-soft);--vp-c-note-1: var(--vp-c-brand-1);--vp-c-note-2: var(--vp-c-brand-2);--vp-c-note-3: var(--vp-c-brand-3);--vp-c-note-soft: var(--vp-c-brand-soft);--vp-c-success-1: var(--vp-c-green-1);--vp-c-success-2: var(--vp-c-green-2);--vp-c-success-3: var(--vp-c-green-3);--vp-c-success-soft: var(--vp-c-green-soft);--vp-c-important-1: var(--vp-c-purple-1);--vp-c-important-2: var(--vp-c-purple-2);--vp-c-important-3: var(--vp-c-purple-3);--vp-c-important-soft: var(--vp-c-purple-soft);--vp-c-warning-1: var(--vp-c-yellow-1);--vp-c-warning-2: var(--vp-c-yellow-2);--vp-c-warning-3: var(--vp-c-yellow-3);--vp-c-warning-soft: var(--vp-c-yellow-soft);--vp-c-danger-1: var(--vp-c-red-1);--vp-c-danger-2: var(--vp-c-red-2);--vp-c-danger-3: var(--vp-c-red-3);--vp-c-danger-soft: var(--vp-c-red-soft);--vp-c-caution-1: var(--vp-c-red-1);--vp-c-caution-2: var(--vp-c-red-2);--vp-c-caution-3: var(--vp-c-red-3);--vp-c-caution-soft: var(--vp-c-red-soft)}:root{--vp-font-family-base: "Inter", ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--vp-font-family-mono: ui-monospace, "Menlo", "Monaco", "Consolas", "Liberation Mono", "Courier New", monospace;font-optical-sizing:auto}:root:where(:lang(zh)){--vp-font-family-base: "Punctuation SC", "Inter", ui-sans-serif, system-ui, "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: 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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
+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-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2 │ (2, 1) a 0.0930075 0.159458")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 3 │ (3, 1) a 0.489525 0.614656")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 4 │ (4, 1) a 0.793832 0.977066")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 5 │ (5, 1) a 0.00191986 0.798656")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 6 │ (6, 1) a 0.861278 0.673027")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 7 │ (7, 1) a 0.207584 0.768342")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 8 │ (8, 1) a 0.0236468 0.0123003")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," ⋮ │ ⋮ ⋮ ⋮ ⋮")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2594 │ (4, 10) z 0.871914 0.19857")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2595 │ (5, 10) z 0.504023 0.694279")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2596 │ (6, 10) z 0.351006 0.206362")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2597 │ (7, 10) z 0.856091 0.842866")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2598 │ (8, 10) z 0.0459964 0.615094")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2599 │ (9, 10) z 0.00516367 0.768924")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2600 │ (10, 10) z 0.8289 0.813696")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#1b7c83","--shiki-dark":"#39c5cf","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2585 rows omitted")])])])],-1)])),_:1})]),_:1}),a[8]||(a[8]=d(`

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"
+ ⋮
+ "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}; diff --git a/previews/PR801/assets/tables.md.CIG0p5h9.lean.js b/previews/PR801/assets/tables.md.CIG0p5h9.lean.js new file mode 100644 index 000000000..a277537cc --- /dev/null +++ b/previews/PR801/assets/tables.md.CIG0p5h9.lean.js @@ -0,0 +1,165 @@ +import{_ as n,B as e,c as g,a4 as d,G as h,w as t,j as i,a as s,o as p}from"./chunks/framework.D3Ia4FEQ.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=e("PluginTabsTab"),k=e("PluginTabs");return p(),g("div",null,[a[6]||(a[6]=d(`

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
+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-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2 │ (2, 1) a 0.0930075 0.159458")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 3 │ (3, 1) a 0.489525 0.614656")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 4 │ (4, 1) a 0.793832 0.977066")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 5 │ (5, 1) a 0.00191986 0.798656")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 6 │ (6, 1) a 0.861278 0.673027")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 7 │ (7, 1) a 0.207584 0.768342")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 8 │ (8, 1) a 0.0236468 0.0123003")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," ⋮ │ ⋮ ⋮ ⋮ ⋮")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2594 │ (4, 10) z 0.871914 0.19857")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2595 │ (5, 10) z 0.504023 0.694279")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2596 │ (6, 10) z 0.351006 0.206362")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2597 │ (7, 10) z 0.856091 0.842866")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2598 │ (8, 10) z 0.0459964 0.615094")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2599 │ (9, 10) z 0.00516367 0.768924")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2600 │ (10, 10) z 0.8289 0.813696")]),s(` +`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#1b7c83","--shiki-dark":"#39c5cf","--shiki-light-font-weight":"bold","--shiki-dark-font-weight":"bold"}}," 2585 rows omitted")])])])],-1)])),_:1})]),_:1}),a[8]||(a[8]=d(`

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"
+ ⋮
+ "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}; diff --git a/previews/PR801/assets/veptoqy.CZKDtjAN.png b/previews/PR801/assets/veptoqy.CZKDtjAN.png new file mode 100644 index 000000000..1443d43f0 Binary files /dev/null and b/previews/PR801/assets/veptoqy.CZKDtjAN.png differ diff --git a/previews/PR801/assets/ybmzrjc.BIa4VhRY.png b/previews/PR801/assets/ybmzrjc.BIa4VhRY.png new file mode 100644 index 000000000..6f0e615af Binary files /dev/null and b/previews/PR801/assets/ybmzrjc.BIa4VhRY.png differ diff --git a/previews/PR801/assets/yhxwiti.CBNkYhiq.png b/previews/PR801/assets/yhxwiti.CBNkYhiq.png new file mode 100644 index 000000000..5128e884c Binary files /dev/null and b/previews/PR801/assets/yhxwiti.CBNkYhiq.png differ diff --git a/previews/PR801/basics.html b/previews/PR801/basics.html new file mode 100644 index 000000000..a4c35da02 --- /dev/null +++ b/previews/PR801/basics.html @@ -0,0 +1,73 @@ + + + + + + DimensionalData.jl + + + + + + + + + + + + + + +
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))
╭─────────────────────────╮
+│ 4×5 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ 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',))
╭─────────────────────────────╮
+│ 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}:
+[:, :, 1] =
+  65  110   71  108  76  15  120   65  121  126
+ 125   78  118  101  11  32   87  108   49  115
+
+[:, :, 2] =
+ 106  84  74  80   9   26  35  74  28  47
+  87   1  42  54  15  107  77  23  89  34
+
+[:, :, 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))
╭─────────────────────────╮
+│ 2×10×3 DimArray{Int8,3} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ channel Categorical{Symbol} [:left, :right] ForwardOrdered,
+  → time    Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ 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
+ + + + \ No newline at end of file diff --git a/previews/PR801/broadcasts.html b/previews/PR801/broadcasts.html new file mode 100644 index 000000000..b121642d7 --- /dev/null +++ b/previews/PR801/broadcasts.html @@ -0,0 +1,162 @@ + + + + + + Dimensional broadcasts with @d and broadcast_dims | DimensionalData.jl + + + + + + + + + + + + + + +
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
+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,
+↗ Ti Dates.DateTime("2000-01-01T00:00:00"):Dates.Month(1):Dates.DateTime("2000-12-01T00:00:00"))

A DimArray from 1:12 to scale with:

julia
julia> month_scalars = DimArray(month, t)
╭────────────────────────────────────────╮
+│ 12-element DimArray{Int64,1} month(Ti) │
+├────────────────────────────────────────┴─────────────────────────────── 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   1
+ 2000-02-01T00:00:00   2
+ 2000-03-01T00:00:00   3
+ 2000-04-01T00:00:00   4
+ 2000-05-01T00:00:00   5
+ 2000-06-01T00:00:00   6
+ 2000-07-01T00:00:00   7
+ 2000-08-01T00:00:00   8
+ 2000-09-01T00:00:00   9
+ 2000-10-01T00:00:00  10
+ 2000-11-01T00:00:00  11
+ 2000-12-01T00:00:00  12

And a larger DimArray for example data:

julia
julia> data = rand(x, y, t)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)))
╭────────────────────────────────╮
+│ 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)))
╭────────────────────────────────╮
+│ 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)
╭───────────────────────────────╮
+│ 100×25×12 DimArray{Float64,3} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 1:25 ForwardOrdered Regular Points,
+  ↗ 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)
╭───────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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)
╭──────────────────────────────────────╮
+│ 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,
+  → X  Sampled{Int64} 1:100 ForwardOrdered Regular Points,
+  ↗ 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
+ + + + \ No newline at end of file diff --git a/previews/PR801/cuda.html b/previews/PR801/cuda.html new file mode 100644 index 000000000..bcf051a89 --- /dev/null +++ b/previews/PR801/cuda.html @@ -0,0 +1,52 @@ + + + + + + CUDA & GPUs | DimensionalData.jl + + + + + + + + + + + + + + +
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
+╭───────────────────────────────╮
+│ 1000×2000 DimArray{Float32,2} │
+├───────────────────────────────┴────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:1000.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 1.0:1.0:2000.0 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────────────┘
+    ↓ →  1.0       2.0        3.0        4.0       …  1998.0        1999.0        2000.0
+    1.0  1.69506   1.28405    0.989952   0.900394        1.73623       1.30427       1.98193
+    2.0  1.73591   0.929995   0.665742   0.345501        0.162919      1.81708       0.702944
+    3.0  1.24575   1.80455    1.78028    1.49097         0.45804       0.224375      0.0197492
+    4.0  0.374026  1.91495    1.17645    0.995683        0.835288      1.54822       0.487601
+    5.0  1.17673   0.0557598  0.183637   1.90645   …     0.88058       1.23788       1.59705
+    6.0  1.57019   0.215049   1.9155     0.982762        0.906838      0.1076        0.390081
+    ⋮                                              ⋱                              
+  995.0  1.48275   0.40409    1.37963    1.66622         0.462981      1.4492        1.26917
+  996.0  1.88869   1.86174    0.298383   0.854739  …     0.778222      1.42151       1.75568
+  997.0  1.88092   1.87436    0.285965   0.304688        1.32669       0.0599431     0.134186
+  998.0  1.18035   1.61025    0.352614   1.75847         0.464554      1.90309       1.30923
+  999.0  1.40584   1.83056    0.0804518  0.177423        1.20779       1.95217       0.881149
+ 1000.0  1.41334   0.719974   0.479126   1.92721         0.0649391     0.642908      1.07277

But the data is on the GPU:

julia
julia> typeof(parent(cuA2))
+CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

GPU Integration goals ​

DimensionalData.jl has two GPU-related goals:

  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.

+ + + + \ No newline at end of file diff --git a/previews/PR801/dimarrays.html b/previews/PR801/dimarrays.html new file mode 100644 index 000000000..7e3797f4c --- /dev/null +++ b/previews/PR801/dimarrays.html @@ -0,0 +1,203 @@ + + + + + + DimArrays | DimensionalData.jl + + + + + + + + + + + + + + +
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))
╭──────────────────────────╮
+│ 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))
╭──────────────────────────╮
+│ 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())
╭───────────────────────────────────╮
+│ 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))
╭─────────────────────────╮
+│ 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]
╭───────────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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))]
╭───────────────────────────────╮
+│ 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}}}:
+ (↓ X 3, → Z 5)
+ (↓ X 7, → Z 4)
+ (↓ X 8, → Z 2)
julia
julia> da2[inds]
╭─────────────────────────╮
+│ 7×3 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────────────────── dims ┐
+  ↓ Y ,
+  → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X, → Z
+└──────────────────────────────────────────────────────────────────────────────┘
+  (3, 5)     (7, 4)    (8, 2)
+ 0.794017   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)]
╭──────────────────────────╮
+│ 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.
+└ @ 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.
+└ @ 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))
╭───────────────────────────╮
+│ 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)
╭───────────────────────────╮
+│ 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
+
+julia> da4 = ones(X(3), Y(3))
╭─────────────────────────╮
+│ 3×3 DimArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ X, → Y
+└────────────────────────────────────────────────────────────────┘
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
+ 1.0  1.0  1.0
julia
julia> @benchmark $da4[X(1), Y(2)]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range (min … max):  3.086 ns … 17.533 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.105 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.110 ns ±  0.232 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+               ▅█            ▅█              ▂             ▂ ▂
+  ▃▁▁▁▁▁▁▁▁▁▁▁▁██▆▁▁▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁▁▁▁▁██▅▁▁▁▁▁▁▁▁▁▁▁██ █
+  3.09 ns      Histogram: log(frequency) by time     3.13 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 … 19.307 ns  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     3.096 ns              ┊ GC (median):    0.00%
+ Time  (mean ± σ):   3.112 ns ±  0.329 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+   █                  ▃                                      
+  ▅█▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▂
+  3.1 ns         Histogram: frequency by time        3.13 ns <
+
+ Memory estimate: 0 bytes, allocs estimate: 0.
+ + + + \ No newline at end of file diff --git a/previews/PR801/dimensions.html b/previews/PR801/dimensions.html new file mode 100644 index 000000000..2667338ab --- /dev/null +++ b/previews/PR801/dimensions.html @@ -0,0 +1,27 @@ + + + + + + Dimensions | DimensionalData.jl + + + + + + + + + + + + + + +
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
+ + + + \ No newline at end of file diff --git a/previews/PR801/diskarrays.html b/previews/PR801/diskarrays.html new file mode 100644 index 000000000..e6f1898b6 --- /dev/null +++ b/previews/PR801/diskarrays.html @@ -0,0 +1,25 @@ + + + + + + DiskArrays.jl compatibility | DimensionalData.jl + + + + + + + + + + + + + + +
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.

+ + + + \ No newline at end of file diff --git a/previews/PR801/extending_dd.html b/previews/PR801/extending_dd.html new file mode 100644 index 000000000..f0223571d --- /dev/null +++ b/previews/PR801/extending_dd.html @@ -0,0 +1,54 @@ + + + + + + Extending DimensionalData | 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
+
+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
+
+Mandatory components
+dims: (defines a `dims` method [true, true],
+       dims are updated on getindex [true, true])
+refdims_base: `refdims` returns a tuple of Dimension or empty [true, true]
+ndims: number of dims matches dimensions of array [true, true]
+size: length of dims matches dimensions of array [true, true]
+rebuild_parent: rebuild parent from args [true, true]
+rebuild_dims: rebuild paaarnet and dims from args [true, true]
+rebuild_parent_kw: rebuild parent from args [true, true]
+rebuild_dims_kw: rebuild dims from args [true, true]
+rebuild: all rebuild arguments and keywords are accepted [true, true]
+
+Optional components
+refdims: (refdims are updated in args rebuild [true, true],
+          refdims are updated in kw rebuild [true, true],
+          dropped dimensions are added to refdims [true, true])
+name: (rebuild updates name in arg rebuild [true, true],
+       rebuild updates name in kw rebuild [true, true])
+metadata: (rebuild updates metadata in arg rebuild [true, true],
+           rebuild updates metadata in kw rebuild [true, true])
+
+Implementation summary:
+  DimArray correctly implements DimensionalData.DimArrayInterface: true
+true
+ + + + \ No newline at end of file diff --git a/previews/PR801/favicon.ico b/previews/PR801/favicon.ico new file mode 100644 index 000000000..37b0a2d5a Binary files /dev/null and b/previews/PR801/favicon.ico differ diff --git a/previews/PR801/get_info.html b/previews/PR801/get_info.html new file mode 100644 index 000000000..04ac3d40f --- /dev/null +++ b/previews/PR801/get_info.html @@ -0,0 +1,53 @@ + + + + + + Getters | 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
+using DimensionalData.Lookups
+x, y = X(10:-1:1), Y(100.0:10:200.0)
(↓ X 10:-1:1,
+→ Y 100.0:10.0:200.0)
julia
julia> A = rand(x, y)
╭───────────────────────────╮
+│ 10×11 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
+  → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  100.0         110.0        …  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,
+→ 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)
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
+ + + + \ No newline at end of file diff --git a/previews/PR801/groupby.html b/previews/PR801/groupby.html new file mode 100644 index 000000000..8b38200f2 --- /dev/null +++ b/previews/PR801/groupby.html @@ -0,0 +1,142 @@ + + + + + + Group By | DimensionalData.jl + + + + + + + + + + + + + + +
DimensionalData.jl

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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
+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}:
+  0
+  1
+  2
+  3
+  4
+  5
+  6
+  7
+  8
+  9
+  ⋮
+ 15
+ 16
+ 17
+ 18
+ 19
+ 20
+ 21
+ 22
+ 23

Tuple groupings

julia
julia> yearmonth.(tempo)
17520-element Vector{Tuple{Int64, Int64}}:
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ (2000, 1)
+ ⋮
+ (2001, 12)
+ (2001, 12)
+ (2001, 12)
+ (2001, 12)
+ (2001, 12)
+ (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))
╭───────────────────────────────╮
+│ 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
+ ⋮                                                 ⋱  ⋮
+ 1.96  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)
╭───────────────────────────────────────────────────╮
+│ 12-element DimGroupByArray{DimArray{Float64,1},1} │
+├───────────────────────────────────────────────────┴──────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+  1  101×1488 DimArray
+  2  101×1368 DimArray
+  3  101×1488 DimArray
+  ⋮
+ 11  101×1440 DimArray
+ 12  101×1464 DimArray

We can take the mean of each group by broadcasting over them:

julia
julia> mean.(groups)
╭────────────────────────────────╮
+│ 12-element DimArray{Float64,1} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ Ti Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>month
+└──────────────────────────────────────────────────────────────────────────────┘
+  1  0.500757
+  2  0.498832
+  3  0.498388
+  4  0.500913
+  ⋮
+ 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))
╭──────────────────────────────────────────────────╮
+│ 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}
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>Bins(month, 4)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ 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))
╭──────────────────────────────────────────────────────╮
+│ 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
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>[DateTime("2000-01-01T00:00:00"), DateTime("2000-01-01T01:00…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ X, → Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ 2000-01-01T00:00:00  101×1 DimArray
+ 2000-01-01T01:00:00  101×1 DimArray
+ 2000-01-01T02:00:00  101×1 DimArray
+ ⋮
+ 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.

+ + + + \ No newline at end of file diff --git a/previews/PR801/hashmap.json b/previews/PR801/hashmap.json new file mode 100644 index 000000000..2dc48d442 --- /dev/null +++ b/previews/PR801/hashmap.json @@ -0,0 +1 @@ +{"api_dimensions.md":"MWHKMaKb","api_lookuparrays.md":"BF14Z7at","api_reference.md":"CQevsPbp","basics.md":"LP9Fcdo1","broadcasts.md":"5J_tBPs5","cuda.md":"Rut_ExXL","dimarrays.md":"7pjFj3JZ","dimensions.md":"CBs16yaQ","diskarrays.md":"64JEAR8Z","extending_dd.md":"DvBZFGy6","get_info.md":"D5VYRM01","groupby.md":"_-rDFlY6","index.md":"Co6U3z_V","integrations.md":"DnteQbzv","object_modification.md":"C6yYw2Ar","plots.md":"BM84STtN","selectors.md":"DMIZn364","stacks.md":"C7pJh52H","tables.md":"CIG0p5h9"} diff --git a/previews/PR801/index.html b/previews/PR801/index.html new file mode 100644 index 000000000..c1b1ee5fc --- /dev/null +++ b/previews/PR801/index.html @@ -0,0 +1,25 @@ + + + + + + DimensionalData.jl + + + + + + + + + + + + + + +
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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.

+ + + + \ No newline at end of file diff --git a/previews/PR801/integrations.html b/previews/PR801/integrations.html new file mode 100644 index 000000000..25ff9664e --- /dev/null +++ b/previews/PR801/integrations.html @@ -0,0 +1,25 @@ + + + + + + Integrations | DimensionalData.jl + + + + + + + + + + + + + + +
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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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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR801/object_modification.html b/previews/PR801/object_modification.html new file mode 100644 index 000000000..f45e8ad0b --- /dev/null +++ b/previews/PR801/object_modification.html @@ -0,0 +1,78 @@ + + + + + + Modifying objects | DimensionalData.jl + + + + + + + + + + + + + + +
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} │
+├──────────────────────┴─────────────────────────────────── dims ┐
+  ↓ X, → Y
+└────────────────────────────────────────────────────────────────┘
+ 0  0  0  0  0
+ 0  0  0  0  0
+ 0  0  0  0  0
julia
julia> parent(A)
3×5 BitMatrix:
+ 0  0  0  0  0
+ 0  0  0  0  0
+ 0  0  0  0  0
julia
julia> A_mod = modify(Array, A)
╭──────────────────────╮
+│ 3×5 DimArray{Bool,2} │
+├──────────────────────┴─────────────────────────────────── dims ┐
+  ↓ X, → Y
+└────────────────────────────────────────────────────────────────┘
+ 0  0  0  0  0
+ 0  0  0  0  0
+ 0  0  0  0  0
julia
julia> parent(A_mod)
3×5 Matrix{Bool}:
+ 0  0  0  0  0
+ 0  0  0  0  0
+ 0  0  0  0  0

reorder ​

reorder is like reverse but declarative, rather than imperative: we tell it how we want the object to be, not what to do.

Reorder a specific dimension

julia
julia> using DimensionalData.Lookups;
+
+julia> A = rand(X(1.0:3.0), Y('a':'n'));
+
+julia> reorder(A, X => ReverseOrdered())
╭──────────────────────────╮
+│ 3×14 DimArray{Float64,2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 3.0:-1.0:1.0 ReverseOrdered Regular Points,
+  → 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)
╭───────────────────────────────────╮
+│ 3×14 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)
╭──────────────────────────╮
+│ 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
+ + + + \ No newline at end of file diff --git a/previews/PR801/plots.html b/previews/PR801/plots.html new file mode 100644 index 000000000..3464ebff6 --- /dev/null +++ b/previews/PR801/plots.html @@ -0,0 +1,29 @@ + + + + + + Plots.jl | DimensionalData.jl + + + + + + + + + + + + + + +
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!

+ + + + \ No newline at end of file diff --git a/previews/PR801/selectors.html b/previews/PR801/selectors.html new file mode 100644 index 000000000..a2d0987f2 --- /dev/null +++ b/previews/PR801/selectors.html @@ -0,0 +1,143 @@ + + + + + + Selectors | DimensionalData.jl + + + + + + + + + + + + + + +
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]))
╭─────────────────────────╮
+│ 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)]
╭─────────────────────────╮
+│ 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])]
╭─────────────────────────╮
+│ 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}
+wrapping: 6-element Vector{Int64}:
+ 13
+  8
+  5
+  3
+  2
+  1

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))
╭─────────────────────────╮
+│ 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))
╭───────────────────────────╮
+│ 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
+ ⋮                                       ⋱               ⋮
+ 1.84   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)]
╭─────────────────────────╮
+│ 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))
╭────────────────────────────╮
+│ 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)]
╭─────────────────────────╮
+│ 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
+ + + + \ No newline at end of file diff --git a/previews/PR801/siteinfo.js b/previews/PR801/siteinfo.js new file mode 100644 index 000000000..eae5db24c --- /dev/null +++ b/previews/PR801/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR801"; diff --git a/previews/PR801/stacks.html b/previews/PR801/stacks.html new file mode 100644 index 000000000..f6b3d91e5 --- /dev/null +++ b/previews/PR801/stacks.html @@ -0,0 +1,94 @@ + + + + + + DimStacks | DimensionalData.jl + + + + + + + + + + + + + + +
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)))
╭───────────────╮
+│ 10×6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+  :a eltype: Float64 dims: X, Y size: 10×6
+  :b eltype: Float64 dims: X, Y size: 10×6
+  :c eltype: Float64 dims: Y size: 6
+  :d eltype: Float64 dims: X size: 10
+└──────────────────────────────────────────────────────────────────────────────┘

The 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
╭────────────────────────────╮
+│ 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]
╭─────────────────────────────────╮
+│ 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)
╭───────────────╮
+│ 10×1 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 7.5:6.0:7.5 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+  :a eltype: Float64 dims: X, Y size: 10×1
+  :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)])
╭───────────────╮
+│ 6×10 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ Y Sampled{Float64} 10.0:-1.0:5.0 ReverseOrdered Regular Points,
+  → X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+  :a eltype: Float64 dims: Y, X size: 6×10
+  :b eltype: Float64 dims: Y, X size: 6×10
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> rotl90(st[(:a, :b)], 2)
╭───────────────╮
+│ 10×6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+  ↓ X Sampled{Float64} 10.0:-1.0:1.0 ReverseOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:-1.0:5.0 ReverseOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── 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
+
+julia> @btime $st[X=1, Y=4]
  4.037 ns (0 allocations: 0 bytes)
+(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
julia
julia> @btime $st[1, 4]
  4.027 ns (0 allocations: 0 bytes)
+(a = 0.2571287355813575, b = 0.925267535912165, c = 0.12713970284423626, d = 0.3258728635315493)
+ + + + \ No newline at end of file diff --git a/previews/PR801/tables.html b/previews/PR801/tables.html new file mode 100644 index 000000000..492d1570d --- /dev/null +++ b/previews/PR801/tables.html @@ -0,0 +1,88 @@ + + + + + + Tables and DataFrames | DimensionalData.jl + + + + + + + + + + + + + + +
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
+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)
╭───────────────────────────────────╮
+│ 10×10×26 DimArray{Float64,3} data │
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ X        Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Y        Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ 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
+  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
+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"
+ ⋮
+ "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"
+ + + + \ No newline at end of file