-
Notifications
You must be signed in to change notification settings - Fork 41
/
dimension.jl
558 lines (453 loc) · 17.5 KB
/
dimension.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
"""
Dimension
Abstract supertype of all dimension types.
Example concrete implementations are [`X`](@ref), [`Y`](@ref), [`Z`](@ref),
[`Ti`](@ref) (Time), and the custom [`Dim`](@ref) dimension.
`Dimension`s 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`](@ref) or [`Sampled`](@ref) as [`Points`](@ref) or
[`Intervals`](@ref) along some transect. DimensionalData will
attempt to guess the lookup from the passed-in index value.
Example:
```jldoctest Dimension
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:
```jldoctest Dimension
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`](@ref).
```jldoctest Dimension
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
```
"""
abstract type Dimension{T} end
"""
IndependentDim <: Dimension
Abstract supertype for independent dimensions. These will plot on the X axis.
"""
abstract type IndependentDim{T} <: Dimension{T} end
"""
DependentDim <: Dimension
Abstract supertype for dependent dimensions. These will plot on the Y axis.
"""
abstract type DependentDim{T} <: Dimension{T} end
"""
XDim <: IndependentDim
Abstract supertype for all X dimensions.
"""
abstract type XDim{T} <: IndependentDim{T} end
"""
YDim <: DependentDim
Abstract supertype for all Y dimensions.
"""
abstract type YDim{T} <: DependentDim{T} end
"""
ZDim <: DependentDim
Abstract supertype for all Z dimensions.
"""
abstract type ZDim{T} <: DependentDim{T} end
"""
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.
`"""
abstract type TimeDim{T} <: IndependentDim{T} end
ConstructionBase.constructorof(d::Type{<:Dimension}) = basetypeof(d)
Adapt.adapt_structure(to, dim::Dimension) = rebuild(dim; val=Adapt.adapt(to, val(dim)))
const DimType = Type{<:Dimension}
const DimTuple = Tuple{Dimension,Vararg{Dimension}}
const SymbolTuple = Tuple{Symbol,Vararg{Symbol}}
const DimTypeTuple = Tuple{DimType,Vararg{DimType}}
const VectorOfDim = Vector{<:Union{Dimension,DimType,Symbol}}
const DimOrDimType = Union{Dimension,DimType,Symbol}
const AllDims = Union{Symbol,Dimension,DimTuple,SymbolTuple,DimType,DimTypeTuple,VectorOfDim}
# DimensionalData interface methods
struct AutoVal{T,K}
val::T
kw::K
end
val(av::AutoVal) = av.val
"""
rebuild(dim::Dimension, val) => Dimension
rebuild(dim::Dimension; val=val(dim)) => Dimension
Rebuild dim with fields from `dim`, and new fields passed in.
"""
function rebuild(dim::D, val) where D <: Dimension
ConstructionBase.constructorof(D)(val)
end
dims(dim::Union{Dimension,DimType,Val{<:Dimension}}) = dim
dims(dims::DimTuple) = dims
dims(::Tuple{}) = ()
dims(x) = nothing
val(dim::Dimension) = dim.val
refdims(x) = ()
lookup(dim::Dimension{<:AbstractArray}) = val(dim)
lookup(dim::Union{DimType,Val{<:Dimension}}) = NoLookup()
lookuptype(dim::Dimension) = typeof(lookup(dim))
lookuptype(::Type{<:Dimension{L}}) where L = L
lookuptype(x) = NoLookup
name(dim::Dimension) = name(typeof(dim))
name(dim::Val{D}) where D = name(D)
name(dim::Type{D}) where D<:Dimension = nameof(D)
label(x) = string(name(x))
# Lookups methods
Lookups.metadata(dim::Dimension) = metadata(lookup(dim))
Lookups.bounds(dim::Dimension) = bounds(val(dim))
Lookups.intervalbounds(dim::Dimension, args...) = intervalbounds(val(dim), args...)
for f in (:shiftlocus, :maybeshiftlocus)
@eval function Lookups.$f(locus::Locus, x; dims=Dimensions.dims(x))
newdims = map(Dimensions.dims(x, dims)) do d
Lookups.$f(locus, d)
end
return setdims(x, newdims)
end
@eval Lookups.$f(locus::Locus, d::Dimension) =
rebuild(d, Lookups.$f(locus, lookup(d)))
end
function hasselection(x, selectors::Union{DimTuple,SelTuple,Selector,Dimension})
hasselection(dims(x), selectors)
end
hasselection(x::Nothing, selectors::Union{DimTuple,SelTuple,Selector,Dimension}) = false
function hasselection(ds::DimTuple, seldims::DimTuple)
sorted = dims(seldims, ds)
hasselection(dims(ds, sorted), map(val, sorted))
end
hasselection(ds::DimTuple, selectors::SelTuple) = all(map(hasselection, ds, selectors))
hasselection(ds::DimTuple, selector::Dimension) = hasselection(dims(ds, selector), selector)
function hasselection(ds::DimTuple, selector::Selector)
throw(ArgumentError("Cannot select from multiple Dimensions with a single Selector"))
end
hasselection(dim::Dimension, seldim::Dimension) = hasselection(dim, val(seldim))
hasselection(dim::Dimension, sel::Selector) = hasselection(lookup(dim), sel)
for func in (:order, :span, :sampling, :locus)
@eval ($func)(dim::Dimension) = ($func)(lookup(dim))
end
# Dispatch on Tuple{<:Dimension}, and map to single dim methods
for f in (:val, :index, :lookup, :metadata, :order, :sampling, :span, :locus, :bounds, :intervalbounds,
:name, :label, :units)
@eval begin
$f(ds::Tuple) = map($f, ds)
$f(::Tuple{}) = ()
$f(ds::Tuple, i1, I...) = $f(ds, (i1, I...))
$f(ds::Tuple, I) = $f(dims(ds, name2dim(I)))
end
end
@inline function selectindices(x, selectors; kw...)
if dims(x) isa Nothing
# This object has no dimensions and no `selectindices` method.
# Just return whatever selectors is, maybe the underlying array can use it.
return selectors
else
# Otherwise select indices based on the object `Dimension`s
return selectindices(dims(x), selectors; kw...)
end
end
@inline selectindices(ds::Tuple, sel...; kw...) = selectindices(ds, sel; kw...)
# Cant get this to compile away without a generated function
# The nothing handling is for if `err=_False`, and we want to combine
# multiple `nothing` into a single `nothing` return value
@generated function selectindices(ds::Tuple, sel::Tuple; kw...)
tuple_exp = Expr(:tuple)
for i in eachindex(ds.parameters)
expr = quote
x = selectindices(ds[$i], sel[$i]; kw...)
isnothing(x) && return nothing
x
end
push!(tuple_exp.args, expr)
end
return tuple_exp
end
@inline selectindices(ds::Tuple, sel::Tuple{}; kw...) = ()
@inline selectindices(dim::Dimension, sel; kw...) = selectindices(val(dim), sel; kw...)
# Deprecated
Lookups.index(dim::Dimension{<:AbstractArray}) = index(val(dim))
Lookups.index(dim::Dimension{<:Val}) = unwrap(index(val(dim)))
# Base methods
const ArrayOrVal = Union{AbstractArray,Val}
Base.parent(d::Dimension) = val(d)
Base.eltype(d::Type{<:Dimension{T}}) where T = T
Base.eltype(d::Type{<:Dimension{A}}) where A<:AbstractArray{T} where T = T
Base.size(d::Dimension, args...) = size(val(d), args...)
Base.axes(d::Dimension) = (val(d) isa DimUnitRange ? val(d) : DimUnitRange(axes(val(d), 1), d),)
Base.axes(d::Dimension, i) = axes(d)[i]
Base.eachindex(d::Dimension) = eachindex(val(d))
Base.length(d::Dimension) = length(val(d))
Base.ndims(d::Dimension) = 0
Base.ndims(d::Dimension{<:AbstractArray}) = ndims(val(d))
Base.iterate(d::Dimension{<:AbstractArray}, args...) = iterate(lookup(d), args...)
Base.first(d::Dimension) = val(d)
Base.first(d::Dimension{<:AbstractArray}) = first(lookup(d))
Base.last(d::Dimension) = val(d)
Base.last(d::Dimension{<:AbstractArray}) = last(lookup(d))
Base.firstindex(d::Dimension) = 1
Base.lastindex(d::Dimension) = 1
Base.firstindex(d::Dimension{<:AbstractArray}) = firstindex(lookup(d))
Base.lastindex(d::Dimension{<:AbstractArray}) = lastindex(lookup(d))
Base.step(d::Dimension) = step(lookup(d))
Base.Array(d::Dimension{<:AbstractArray}) = collect(lookup(d))
function Base.:(==)(d1::Dimension, d2::Dimension)
basetypeof(d1) == basetypeof(d2) && val(d1) == val(d2)
end
LookupArrays.ordered_first(d::Dimension{<:AbstractArray}) = ordered_first(lookup(d))
LookupArrays.ordered_last(d::Dimension{<:AbstractArray}) = ordered_last(lookup(d))
LookupArrays.ordered_firstindex(d::Dimension{<:AbstractArray}) = ordered_firstindex(lookup(d))
LookupArrays.ordered_lastindex(d::Dimension{<:AbstractArray}) = ordered_lastindex(lookup(d))
Base.size(dims::DimTuple) = map(length, dims)
Base.CartesianIndices(dims::DimTuple) = CartesianIndices(map(d -> axes(d, 1), dims))
# Extents.jl
function Extents.extent(ds::DimTuple, args...)
extent_dims = _astuple(dims(ds, args...))
extent_bounds = bounds(extent_dims)
return Extents.Extent{name(extent_dims)}(extent_bounds)
end
dims(extent::Extents.Extent{K}) where K = map(rebuild, name2dim(K), values(extent))
dims(extent::Extents.Extent, ds) = dims(dims(extent), ds)
# Produce a 2 * length(dim) matrix of interval bounds from a dim
dim2boundsmatrix(dim::Dimension) = dim2boundsmatrix(lookup(dim))
function dim2boundsmatrix(lookup::Lookup)
samp = sampling(lookup)
samp isa Intervals || error("Cannot create a bounds matrix for $(nameof(typeof(samp)))")
_dim2boundsmatrix(locus(lookup), span(lookup), lookup)
end
_dim2boundsmatrix(::Locus, span::Explicit, lookup) = val(span)
function _dim2boundsmatrix(::Locus, span::Regular, lookup)
# Only offset starts and reuse them for ends,
# so floating point error is the same.
starts = Lookups._shiftlocus(Start(), lookup)
dest = Array{eltype(starts),2}(undef, 2, length(starts))
# Use `bounds` as the start/end values
if order(lookup) isa ReverseOrdered
for i in 1:length(starts) - 1
dest[1, i] = dest[2, i + 1] = starts[i + firstindex(starts) - 1]
end
dest[1, end], dest[2, 1] = bounds(lookup)
else
for i in 1:length(starts) - 1
dest[1, i + 1] = dest[2, i] = starts[i + firstindex(starts)]
end
dest[1, 1], dest[2, end] = bounds(lookup)
end
return dest
end
@noinline _dim2boundsmatrix(::Center, span::Regular{Dates.TimeType}, lookupj) =
error("Cannot convert a Center TimeType index to Explicit automatically: use a bounds matrix e.g. Explicit(bnds)")
@noinline _dim2boundsmatrix(::Start, span::Irregular, lookupj) =
error("Cannot convert Irregular to Explicit automatically: use a bounds matrix e.g. Explicit(bnds)")
"""
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.
```jldoctest; setup = :(using DimensionalData)
julia> dim = Dim{:custom}(['a', 'b', 'c'])
custom ['a', 'b', 'c']
```
"""
struct Dim{S,T} <: Dimension{T}
val::T
function Dim{S}(val; kw...) where {S}
if length(kw) > 0
val = AutoVal(val, values(kw))
end
new{S,typeof(val)}(val)
end
function Dim{S}(val::AbstractArray; kw...) where S
if length(kw) > 0
val = AutoLookup(val, values(kw))
end
Dim{S,typeof(val)}(val)
end
function Dim{S,T}(val::T) where {S,T}
new{S,T}(val)
end
end
Dim{S}() where S = Dim{S}(:)
name(::Type{<:Dim{S}}) where S = S
basetypeof(::Type{<:Dim{S}}) where S = Dim{S}
name2dim(s::Val{S}) where S = Dim{S}()
"""
AnonDim <: Dimension
AnonDim()
Anonymous dimension. Used when extra dimensions are created,
such as during transpose of a vector.
"""
struct AnonDim{T} <: Dimension{T}
val::T
end
AnonDim() = AnonDim(Colon())
AnonDim(val, arg1, args...) = AnonDim(val)
metadata(::AnonDim) = NoMetadata()
"""
@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:
```jldoctest
using DimensionalData
using DimensionalData: @dim, YDim, XDim
@dim Lat YDim "Latitude"
@dim Lon XDim "Longitude"
# output
```
"""
macro dim end
macro dim(typ::Symbol, args...)
dimmacro(typ::Symbol, Dimension, args...)
end
macro dim(typ::Symbol, supertyp::Symbol, args...)
dimmacro(typ, supertyp, args...)
end
function dimmacro(typ, supertype, label::String=string(typ))
quote
Base.@__doc__ struct $typ{T} <: $supertype{T}
val::T
function $typ(val; kw...)
if length(kw) > 0
val = $Dimensions.AutoVal(val, values(kw))
end
new{typeof(val)}(val)
end
$typ{T}(val::T; kw...) where T = new(val::T)
end
function $typ(val::AbstractArray; kw...)
if length(kw) > 0
val = $Dimensions.AutoLookup(val, values(kw))
end
$typ{typeof(val)}(val)
end
$typ() = $typ(:)
$Dimensions.name(::Type{<:$typ}) = $(QuoteNode(Symbol(typ)))
$Dimensions.name2dim(::Val{$(QuoteNode(typ))}) = $typ()
$Dimensions.label(::$typ) = $label
$Dimensions.label(::Type{<:$typ}) = $label
end |> esc
end
# Define some common dimensions.
"""
X <: XDim
X(val=:)
X [`Dimension`](@ref). `X <: XDim <: IndependentDim`
## Examples
```julia
xdim = X(2:2:10)
```
```julia
val = A[X(1)]
```
```julia
mean(A; dims=X)
```
"""
@dim X XDim
"""
Y <: YDim
Y(val=:)
Y [`Dimension`](@ref). `Y <: YDim <: DependentDim`
## Examples
```julia
ydim = Y(['a', 'b', 'c'])
```
```julia
val = A[Y(1)]
```
```julia
mean(A; dims=Y)
```
"""
@dim Y YDim
"""
Z <: ZDim
Z(val=:)
Z [`Dimension`](@ref). `Z <: ZDim <: Dimension`
## Example:
```julia
zdim = Z(10:10:100)
```
```julia
val = A[Z(1)]
```
```julia
mean(A; dims=Z)
```
"""
@dim Z ZDim
"""m
Ti <: TimeDim
Ti(val=:)
Time [`Dimension`](@ref). `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)
```
"""
@dim Ti TimeDim "Time"
const Time = Ti # For some backwards compat