-
-
Notifications
You must be signed in to change notification settings - Fork 5.4k
/
multidimensional.jl
1159 lines (1017 loc) · 42.4 KB
/
multidimensional.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
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# This file is a part of Julia. License is MIT: http:https://julialang.org/license
### Multidimensional iterators
module IteratorsMD
import Base: eltype, length, size, start, done, next, last, in, getindex,
setindex!, linearindexing, min, max, zero, one, isless, eachindex,
ndims, iteratorsize, to_index
importall ..Base.Operators
import Base: simd_outer_range, simd_inner_length, simd_index
using Base: LinearFast, LinearSlow, AbstractCartesianIndex, fill_to_length, tail
export CartesianIndex, CartesianRange
# CartesianIndex
immutable CartesianIndex{N} <: AbstractCartesianIndex{N}
I::NTuple{N,Int}
CartesianIndex(index::NTuple{N,Integer}) = new(index)
end
CartesianIndex{N}(index::NTuple{N,Integer}) = CartesianIndex{N}(index)
(::Type{CartesianIndex})(index::Integer...) = CartesianIndex(index)
(::Type{CartesianIndex{N}}){N}(index::Vararg{Integer,N}) = CartesianIndex{N}(index)
# Allow passing tuples smaller than N
(::Type{CartesianIndex{N}}){N}(index::Tuple) = CartesianIndex{N}(fill_to_length(index, 1, Val{N}))
(::Type{CartesianIndex{N}}){N}(index::Integer...) = CartesianIndex{N}(index)
(::Type{CartesianIndex{N}}){N}() = CartesianIndex{N}(())
# Un-nest passed CartesianIndexes
CartesianIndex(index::Union{Integer, CartesianIndex}...) = CartesianIndex(flatten(index))
flatten(I::Tuple{}) = I
flatten(I::Tuple{Any}) = I
flatten{N}(I::Tuple{CartesianIndex{N}}) = I[1].I
@inline flatten(I) = _flatten(I...)
@inline _flatten() = ()
@inline _flatten(i, I...) = (i, _flatten(I...)...)
@inline _flatten(i::CartesianIndex, I...) = (i.I..., _flatten(I...)...)
CartesianIndex(index::Tuple{Vararg{Union{Integer, CartesianIndex}}}) = CartesianIndex(index...)
# length
length{N}(::CartesianIndex{N})=N
length{N}(::Type{CartesianIndex{N}})=N
# indexing
getindex(index::CartesianIndex, i::Integer) = index.I[i]
# zeros and ones
zero{N}(::CartesianIndex{N}) = zero(CartesianIndex{N})
zero{N}(::Type{CartesianIndex{N}}) = CartesianIndex(ntuple(x -> 0, Val{N}))
one{N}(::CartesianIndex{N}) = one(CartesianIndex{N})
one{N}(::Type{CartesianIndex{N}}) = CartesianIndex(ntuple(x -> 1, Val{N}))
# arithmetic, min/max
(-){N}(index::CartesianIndex{N}) = CartesianIndex{N}(map(-, index.I))
(+){N}(index1::CartesianIndex{N}, index2::CartesianIndex{N}) = CartesianIndex{N}(map(+, index1.I, index2.I))
(-){N}(index1::CartesianIndex{N}, index2::CartesianIndex{N}) = CartesianIndex{N}(map(-, index1.I, index2.I))
min{N}(index1::CartesianIndex{N}, index2::CartesianIndex{N}) = CartesianIndex{N}(map(min, index1.I, index2.I))
max{N}(index1::CartesianIndex{N}, index2::CartesianIndex{N}) = CartesianIndex{N}(map(max, index1.I, index2.I))
(+){N}(index::CartesianIndex{N}, i::Integer) = CartesianIndex{N}(map(x->x+i, index.I))
(+){N}(i::Integer, index::CartesianIndex{N}) = index+i
(-){N}(index::CartesianIndex{N}, i::Integer) = CartesianIndex{N}(map(x->x-i, index.I))
(-){N}(i::Integer, index::CartesianIndex{N}) = CartesianIndex{N}(map(x->i-x, index.I))
(*){N}(a::Integer, index::CartesianIndex{N}) = CartesianIndex{N}(map(x->a*x, index.I))
(*)(index::CartesianIndex,a::Integer)=*(a,index)
# comparison
@inline isless{N}(I1::CartesianIndex{N}, I2::CartesianIndex{N}) = _isless(0, I1.I, I2.I)
@inline function _isless{N}(ret, I1::NTuple{N,Int}, I2::NTuple{N,Int})
newret = ifelse(ret==0, icmp(I1[N], I2[N]), ret)
_isless(newret, Base.front(I1), Base.front(I2))
end
_isless(ret, ::Tuple{}, ::Tuple{}) = ifelse(ret==1, true, false)
icmp(a, b) = ifelse(isless(a,b), 1, ifelse(a==b, 0, -1))
# Iteration
immutable CartesianRange{I<:CartesianIndex}
start::I
stop::I
end
CartesianRange{N}(index::CartesianIndex{N}) = CartesianRange(one(index), index)
CartesianRange(::Tuple{}) = CartesianRange{CartesianIndex{0}}(CartesianIndex{0}(()),CartesianIndex{0}(()))
CartesianRange{N}(sz::NTuple{N,Int}) = CartesianRange(CartesianIndex(sz))
CartesianRange{N}(rngs::NTuple{N,Union{Integer,AbstractUnitRange}}) = CartesianRange(CartesianIndex(map(first, rngs)), CartesianIndex(map(last, rngs)))
ndims(R::CartesianRange) = length(R.start)
ndims{I<:CartesianIndex}(::Type{CartesianRange{I}}) = length(I)
eachindex(::LinearSlow, A::AbstractArray) = CartesianRange(indices(A))
@inline eachindex(::LinearSlow, A::AbstractArray, B::AbstractArray...) = CartesianRange(maxsize((), A, B...))
maxsize(sz) = sz
@inline maxsize(sz, A, B...) = maxsize(maxt(sz, size(A)), B...)
@inline maxt(a::Tuple{}, b::Tuple{}) = ()
@inline maxt(a::Tuple{}, b::Tuple) = b
@inline maxt(a::Tuple, b::Tuple{}) = a
@inline maxt(a::Tuple, b::Tuple) = (max(a[1], b[1]), maxt(tail(a), tail(b))...)
eltype{I}(::Type{CartesianRange{I}}) = I
iteratorsize{I}(::Type{CartesianRange{I}}) = Base.HasShape()
@inline function start{I<:CartesianIndex}(iter::CartesianRange{I})
if any(map(>, iter.start.I, iter.stop.I))
return iter.stop+1
end
iter.start
end
@inline function next{I<:CartesianIndex}(iter::CartesianRange{I}, state)
state, I(inc(state.I, iter.start.I, iter.stop.I))
end
# increment & carry
@inline inc(::Tuple{}, ::Tuple{}, ::Tuple{}) = ()
@inline inc(state::Tuple{Int}, start::Tuple{Int}, stop::Tuple{Int}) = (state[1]+1,)
@inline function inc(state, start, stop)
if state[1] < stop[1]
return (state[1]+1,tail(state)...)
end
newtail = inc(tail(state), tail(start), tail(stop))
(start[1], newtail...)
end
@inline done{I<:CartesianIndex}(iter::CartesianRange{I}, state) = state.I[end] > iter.stop.I[end]
# 0-d cartesian ranges are special-cased to iterate once and only once
start{I<:CartesianIndex{0}}(iter::CartesianRange{I}) = false
next{I<:CartesianIndex{0}}(iter::CartesianRange{I}, state) = iter.start, true
done{I<:CartesianIndex{0}}(iter::CartesianRange{I}, state) = state
size{I<:CartesianIndex}(iter::CartesianRange{I}) = map(dimlength, iter.start.I, iter.stop.I)
dimlength(start, stop) = stop-start+1
length(iter::CartesianRange) = prod(size(iter))
last(iter::CartesianRange) = iter.stop
to_index(c::CartesianIndex) = c
@inline function in{I<:CartesianIndex}(i::I, r::CartesianRange{I})
_in(true, i.I, r.start.I, r.stop.I)
end
_in(b, ::Tuple{}, ::Tuple{}, ::Tuple{}) = b
@inline _in(b, i, start, stop) = _in(b & (start[1] <= i[1] <= stop[1]), tail(i), tail(start), tail(stop))
simd_outer_range(iter::CartesianRange{CartesianIndex{0}}) = iter
function simd_outer_range{I}(iter::CartesianRange{I})
start = CartesianIndex(tail(iter.start.I))
stop = CartesianIndex(tail(iter.stop.I))
CartesianRange(start, stop)
end
simd_inner_length{I<:CartesianIndex{0}}(iter::CartesianRange{I}, ::CartesianIndex) = 1
simd_inner_length(iter::CartesianRange, I::CartesianIndex) = iter.stop[1]-iter.start[1]+1
simd_index{I<:CartesianIndex{0}}(iter::CartesianRange{I}, ::CartesianIndex, I1::Int) = iter.start
@inline function simd_index{N}(iter::CartesianRange, Ilast::CartesianIndex{N}, I1::Int)
CartesianIndex((I1+iter.start[1], Ilast.I...))
end
# Split out the first N elements of a tuple
@inline split{N}(t, V::Type{Val{N}}) = _split((), t, V)
@inline _split(tN, trest, V) = _split((tN..., trest[1]), tail(trest), V)
# exit either when we've exhausted the input tuple or when tN has length N
@inline _split{N}(tN::NTuple{N}, ::Tuple{}, ::Type{Val{N}}) = tN, () # ambig.
@inline _split{N}(tN, ::Tuple{}, ::Type{Val{N}}) = tN, ()
@inline _split{N}(tN::NTuple{N}, trest, ::Type{Val{N}}) = tN, trest
end # IteratorsMD
using .IteratorsMD
## Support for SubArray with arrays of CartesianIndex
function _indices_sub{N}(S::SubArray, pinds, i1::AbstractArray{CartesianIndex{N}}, I...)
@_inline_meta
(unsafe_indices(i1)..., _indices_sub(S, IteratorsMD.split(pinds, Val{N})[2], I...)...)
end
## Bounds-checking with CartesianIndex
@inline checkbounds_indices(::Type{Bool}, ::Tuple{}, I::Tuple{CartesianIndex,Vararg{Any}}) =
checkbounds_indices(Bool, (), (I[1].I..., tail(I)...))
@inline checkbounds_indices(::Type{Bool}, IA::Tuple{Any}, I::Tuple{CartesianIndex,Vararg{Any}}) =
checkbounds_indices(Bool, IA, (I[1].I..., tail(I)...))
@inline checkbounds_indices(::Type{Bool}, IA::Tuple, I::Tuple{CartesianIndex,Vararg{Any}}) =
checkbounds_indices(Bool, IA, (I[1].I..., tail(I)...))
# Support indexing with an array of CartesianIndex{N}s
# Here we try to consume N of the indices (if there are that many available)
# The first two simply handle ambiguities
@inline function checkbounds_indices{N}(::Type{Bool}, ::Tuple{}, I::Tuple{AbstractArray{CartesianIndex{N}},Vararg{Any}})
checkindex(Bool, (), I[1]) & checkbounds_indices(Bool, (), tail(I))
end
@inline function checkbounds_indices{N}(::Type{Bool}, IA::Tuple{Any}, I::Tuple{AbstractArray{CartesianIndex{N}},Vararg{Any}})
checkindex(Bool, IA, I[1]) & checkbounds_indices(Bool, (), tail(I))
end
@inline function checkbounds_indices{N}(::Type{Bool}, IA::Tuple, I::Tuple{AbstractArray{CartesianIndex{N}},Vararg{Any}})
IA1, IArest = IteratorsMD.split(IA, Val{N})
checkindex(Bool, IA1, I[1]) & checkbounds_indices(Bool, IArest, tail(I))
end
function checkindex{N}(::Type{Bool}, inds::Tuple, I::AbstractArray{CartesianIndex{N}})
b = true
for i in I
b &= checkbounds_indices(Bool, inds, (i,))
end
b
end
# combined count of all indices, including CartesianIndex and
# AbstractArray{CartesianIndex}
# rather than returning N, it returns an NTuple{N,Bool} so the result is inferrable
@inline index_ndims(i1, I...) = (true, index_ndims(I...)...)
@inline function index_ndims{N}(i1::CartesianIndex{N}, I...)
(map(x->true, i1.I)..., index_ndims(I...)...)
end
@inline function index_ndims{N}(i1::AbstractArray{CartesianIndex{N}}, I...)
(ntuple(x->true, Val{N})..., index_ndims(I...)...)
end
index_ndims() = ()
# Recursively compute the lengths of a list of indices, without dropping scalars
# These need to be inlined for more than 3 indexes
# Trailing CartesianIndex{0}s and arrays thereof are strange when used as
# trailing indexes -- they behave as though they were never there for the
# purposes of generalized linear indexing.
typealias CI0 Union{CartesianIndex{0}, AbstractArray{CartesianIndex{0}}}
index_lengths(A::AbstractArray, I::Colon) = (_length(A),)
@inline index_lengths(A::AbstractArray, I...) = index_lengths_dim(A, 1, I...)
index_lengths_dim(A, dim) = ()
index_lengths_dim(A, dim, ::Colon) = (trailingsize(indices(A), dim),)
index_lengths_dim(A, dim, ::Colon, i::CI0, I::CI0...) = (trailingsize(indices(A), dim), index_lengths_dim(A, dim+1, i, I...)...)
@inline index_lengths_dim(A, dim, ::Colon, i, I...) = (_length(indices(A, dim)), index_lengths_dim(A, dim+1, i, I...)...)
@inline index_lengths_dim(A, dim, ::Real, I...) = (1, index_lengths_dim(A, dim+1, I...)...)
@inline index_lengths_dim{N}(A, dim, ::CartesianIndex{N}, I...) = (1, index_lengths_dim(A, dim+N, I...)...)
@inline index_lengths_dim(A, dim, i::AbstractArray, I...) = (length(i), index_lengths_dim(A, dim+1, I...)...)
@inline index_lengths_dim(A, dim, i::AbstractArray{Bool}, I...) = (sum(i), index_lengths_dim(A, dim+1, I...)...)
@inline index_lengths_dim{N}(A, dim, i::AbstractArray{CartesianIndex{N}}, I...) = (length(i), index_lengths_dim(A, dim+N, I...)...)
# shape of array to create for getindex() with indexes I, dropping scalars
# returns a Tuple{Vararg{AbstractUnitRange}} of indices
index_shape(A::AbstractArray, I::Colon) = (linearindices(A),)
@inline index_shape(A::AbstractArray, I...) = index_shape_dim(indices(A), I...)
@inline index_shape_dim(inds::Tuple{Any}, ::Colon) = inds
@inline index_shape_dim(inds, ::Colon) = (OneTo(trailingsize(inds)),)
@inline index_shape_dim(inds, ::Colon, i::CI0, I::CI0...) =
(OneTo(trailingsize(inds)), index_shape_dim((), i, I...)...)
@inline function index_shape_dim(inds, ::Colon, i, I...)
inds1, indstail = IteratorsMD.split(inds, Val{1})
(inds1..., index_shape_dim(indstail, i, I...)...)
end
@inline index_shape_dim(inds, ::Real...) = ()
@inline index_shape_dim(inds, ::Real, I...) = index_shape_dim(safe_tail(inds), I...)
@inline index_shape_dim{N}(inds, ::CartesianIndex{N}, I...) =
index_shape_dim(IteratorsMD.split(inds, Val{N})[2], I...)
@inline index_shape_dim(inds, i::AbstractArray, I...) =
(indices(i)..., index_shape_dim(safe_tail(inds), I...)...)
@inline index_shape_dim(inds, i::AbstractArray{Bool}, I...) =
(OneTo(sum(i)), index_shape_dim(safe_tail(inds), I...)...)
# single CartesianIndex version not needed because of call to flatten in _getindex...
# ...but array of CartesianIndex is not covered
@inline function index_shape_dim{N}(inds, i::AbstractArray{CartesianIndex{N}}, I...)
indsN, indstail = IteratorsMD.split(inds, Val{N})
(indices(i)..., index_shape_dim(indstail, I...)...)
end
# Convert Colon indices into explicit indices
@inline decolon(A::AbstractArray, ::Colon) = (linearindices(A),)
@inline decolon(A::AbstractArray, I...) = decolon_dim(indices(A), I...)
@inline decolon_dim(inds) = ()
@inline decolon_dim(inds::Tuple{Any}, ::Colon) = inds
@inline decolon_dim(inds, ::Colon) = (OneTo(trailingsize(inds)),)
@inline decolon_dim(inds, ::Colon, i::CI0, I::CI0...) = (OneTo(trailingsize(inds)), i, I...)
@inline function decolon_dim(inds, ::Colon, I...)
inds1, indstail = IteratorsMD.split(inds, Val{1})
(maybe_oneto(inds1...), decolon_dim(indstail, I...)...)
end
@inline decolon_dim(inds, i1, I...) = (i1, decolon_dim(safe_tail(inds), I...)...)
@inline function decolon_dim{N}(inds, i1::AbstractArray{CartesianIndex{N}}, I...)
indsN, indstail = IteratorsMD.split(inds, Val{N})
(i1, decolon_dim(indstail, I...)...)
end
maybe_oneto(i) = i
maybe_oneto() = OneTo(1)
### From abstractarray.jl: Internal multidimensional indexing definitions ###
getindex(x::Number, i::CartesianIndex{0}) = x
getindex(t::Tuple, I...) = getindex(t, IteratorsMD.flatten(I)...)
# These are not defined on directly on getindex to avoid
# ambiguities for AbstractArray subtypes. See the note in abstractarray.jl
# Note that it's most efficient to call checkbounds first, and then to_index
@inline function _getindex{T,N}(l::LinearIndexing, A::AbstractArray{T,N}, I::Vararg{Union{Real, AbstractArray, Colon},N})
@boundscheck checkbounds(A, I...)
_unsafe_getindex(l, A, I...)
end
# Explicitly allow linear indexing with one non-scalar index
@inline function _getindex(l::LinearIndexing, A::AbstractArray, i::Union{Real, AbstractArray, Colon})
@boundscheck checkbounds(A, i)
_unsafe_getindex(l, _maybe_reshape(l, A, (i,)), i)
end
# But we can speed up LinearSlow arrays by reshaping them to vectors:
_maybe_reshape(::LinearFast, A::AbstractArray, i) = A
_maybe_reshape(::LinearSlow, A::AbstractVector, i) = A
@inline _maybe_reshape(::LinearSlow, A::AbstractArray, i) = _maybe_reshape(LinearSlow(), index_ndims(i...), A)
@inline _maybe_reshape{T,N}(::LinearIndexing, ::NTuple{N}, A::AbstractArray{T,N}) = A
@inline _maybe_reshape{N}(::LinearIndexing, ::NTuple{N}, A) = reshape(A, Val{N})
@inline function _getindex{N}(l::LinearIndexing, A::AbstractArray, I::Vararg{Union{Real, AbstractArray, Colon},N}) # TODO: DEPRECATE FOR #14770
@boundscheck checkbounds(A, I...)
_unsafe_getindex(l, _maybe_reshape(l, A, I), I...)
end
@generated function _unsafe_getindex(::LinearIndexing, A::AbstractArray, I::Union{Real, AbstractArray, Colon}...)
N = length(I)
quote
# This is specifically *not* inlined.
@nexprs $N d->(I_d = to_index(I[d]))
shape = @ncall $N index_shape A I
dest = similar(A, shape)
map(unsafe_length, indices(dest)) == map(unsafe_length, shape) || throw_checksize_error(dest, shape)
@ncall $N _unsafe_getindex! dest A I
end
end
# logical indexing optimization - don't use find (within to_index)
function _unsafe_getindex(::LinearIndexing, src::AbstractArray, I::AbstractArray{Bool})
shape = index_shape(src, I)
dest = similar(src, shape)
map(unsafe_length, indices(dest)) == map(unsafe_length, shape) || throw_checksize_error(dest, shape)
D = eachindex(dest)
Ds = start(D)
for (b, s) in zip(I, eachindex(src))
@inbounds if b
d, Ds = next(D, Ds)
dest[d] = src[s]
end
end
dest
end
# specialized form for LinearFast
function _unsafe_getindex(::LinearFast, src::AbstractArray, I::AbstractArray{Bool})
shape = index_shape(src, I)
dest = similar(src, shape)
map(unsafe_length, indices(dest)) == map(unsafe_length, shape) || throw_checksize_error(dest, shape)
D = eachindex(dest)
Ds = start(D)
s = first(linearindices(src))-1
for i in eachindex(I)
s += 1
@inbounds if I[i]
d, Ds = next(D, Ds)
dest[d] = src[s]
end
end
dest
end
# Always index with the exactly indices provided.
@generated function _unsafe_getindex!(dest::AbstractArray, src::AbstractArray, I::Union{Real, AbstractArray, Colon}...)
N = length(I)
quote
$(Expr(:meta, :inline))
@nexprs $N d->(I_d = I[d])
J = @ncall $N decolon src I
@nexprs $N d->(J_d = J[d])
D = eachindex(dest)
Ds = start(D)
@inbounds @nloops $N j d->J_d begin
d, Ds = next(D, Ds)
dest[d] = @ncall $N getindex src j
end
dest
end
end
@noinline throw_checksize_error(A, sz) = throw(DimensionMismatch("output array is the wrong size; expected $sz, got $(size(A))"))
## setindex! ##
# For multi-element setindex!, we check bounds, convert the indices (to_index),
# and ensure the value to set is either an AbstractArray or a Repeated scalar
# before redispatching to the _unsafe_batchsetindex!
_iterable(v::AbstractArray) = v
_iterable(v) = Iterators.repeated(v)
@inline function _setindex!{T,N}(l::LinearIndexing, A::AbstractArray{T,N}, x, J::Vararg{Union{Real,AbstractArray,Colon},N})
@boundscheck checkbounds(A, J...)
_unsafe_setindex!(l, A, x, J...)
end
@inline function _setindex!(l::LinearIndexing, A::AbstractArray, x, j::Union{Real,AbstractArray,Colon})
@boundscheck checkbounds(A, j)
_unsafe_setindex!(l, _maybe_reshape(l, A, (j,)), x, j)
A
end
@inline function _setindex!{N}(l::LinearIndexing, A::AbstractArray, x, J::Vararg{Union{Real, AbstractArray, Colon},N}) # TODO: DEPRECATE FOR #14770
@boundscheck checkbounds(A, J...)
_unsafe_setindex!(l, _maybe_reshape(l, A, J), x, J...)
A
end
@inline function _unsafe_setindex!(::LinearIndexing, A::AbstractArray, x, J::Union{Real,AbstractArray,Colon}...)
_unsafe_batchsetindex!(A, _iterable(x), to_indexes(J...)...)
end
# 1-d logical indexing: override the above to avoid calling find (in to_index)
function _unsafe_setindex!(::LinearIndexing, A::AbstractArray, x, I::AbstractArray{Bool})
X = _iterable(x)
Xs = start(X)
c = 0
@inbounds for (iA, i) in zip(eachindex(A), eachindex(I))
Ii = I[i]
if Ii
done(X, Xs) && throw_setindex_mismatch(x, c+1)
(v, Xs) = next(X, Xs)
A[iA] = v
c += 1
end
end
setindex_shape_check(X, c)
A
end
# specialized form for LinearFast
function _unsafe_setindex!(::LinearFast, A::AbstractArray, x, I::AbstractArray{Bool})
X = _iterable(x)
Xs = start(X)
iA = 0
c = 0
for i in eachindex(I)
iA += 1
@inbounds if I[i]
done(X, Xs) && throw_setindex_mismatch(x, c+1)
(v, Xs) = next(X, Xs)
A[iA] = v
c += 1
end
end
setindex_shape_check(X, c)
A
end
@generated function _unsafe_batchsetindex!(A::AbstractArray, X, I::Union{Real,AbstractArray,Colon}...)
N = length(I)
quote
@nexprs $N d->(I_d = I[d])
idxlens = @ncall $N index_lengths A I
@ncall $N setindex_shape_check X (d->idxlens[d])
J = @ncall $N decolon A I
@nexprs $N d->(J_d = J[d])
Xs = start(X)
@inbounds @nloops $N j d->J_d begin
v, Xs = next(X, Xs)
@ncall $N setindex! A v j
end
A
end
end
@propagate_inbounds function _getindex{T,N}(l::LinearIndexing, A::AbstractArray{T,N}, I::Union{Real,AbstractArray,Colon,CartesianIndex}...)
getindex(A, IteratorsMD.flatten(I)...)
end
@propagate_inbounds function _setindex!{T,N}(l::LinearIndexing, A::AbstractArray{T,N}, v, I::Union{Real,AbstractArray,Colon,CartesianIndex}...)
setindex!(A, v, IteratorsMD.flatten(I)...)
end
##
@generated function findn{T,N}(A::AbstractArray{T,N})
quote
nnzA = countnz(A)
@nexprs $N d->(I_d = Array{Int}(nnzA))
k = 1
@nloops $N i A begin
@inbounds if (@nref $N A i) != zero(T)
@nexprs $N d->(I_d[k] = i_d)
k += 1
end
end
@ntuple $N I
end
end
for (f, fmod, op) = ((:cummin, :_cummin!, :min), (:cummax, :_cummax!, :max))
@eval function ($f)(v::AbstractVector)
n = length(v)
cur_val = v[1]
res = similar(v, n)
res[1] = cur_val
for i in 2:n
cur_val = ($op)(v[i], cur_val)
res[i] = cur_val
end
return res
end
@eval function ($f)(A::AbstractArray, axis::Integer)
axis > 0 || throw(ArgumentError("axis must be a positive integer"))
res = similar(A)
axis > ndims(A) && return copy!(res, A)
inds = indices(A)
if isempty(inds[axis])
return res
end
R1 = CartesianRange(inds[1:axis-1])
R2 = CartesianRange(inds[axis+1:end])
($fmod)(res, A, R1, R2, axis)
end
@eval @noinline function ($fmod)(res, A::AbstractArray, R1::CartesianRange, R2::CartesianRange, axis::Integer)
inds = indices(A, axis)
i1 = first(inds)
for I2 in R2
for I1 in R1
res[I1, i1, I2] = A[I1, i1, I2]
end
for i = i1+1:last(inds)
for I1 in R1
res[I1, i, I2] = ($op)(A[I1, i, I2], res[I1, i-1, I2])
end
end
end
res
end
@eval ($f)(A::AbstractArray) = ($f)(A, 1)
end
"""
cumsum(A, dim=1)
Cumulative sum along a dimension `dim` (defaults to 1). See also [`cumsum!`](:func:`cumsum!`)
to use a preallocated output array, both for performance and to control the precision of the
output (e.g. to avoid overflow).
```jldoctest
julia> a = [1 2 3; 4 5 6]
2×3 Array{Int64,2}:
1 2 3
4 5 6
julia> cumsum(a,1)
2×3 Array{Int64,2}:
1 2 3
5 7 9
julia> cumsum(a,2)
2×3 Array{Int64,2}:
1 3 6
4 9 15
```
"""
cumsum{T}(A::AbstractArray{T}, axis::Integer=1) = cumsum!(similar(A, Base.rcum_promote_type(+, T)), A, axis)
cumsum!(B, A::AbstractArray) = cumsum!(B, A, 1)
"""
cumprod(A, dim=1)
Cumulative product along a dimension `dim` (defaults to 1). See also
[`cumprod!`](:func:`cumprod!`) to use a preallocated output array, both for performance and
to control the precision of the output (e.g. to avoid overflow).
```jldoctest
julia> a = [1 2 3; 4 5 6]
2×3 Array{Int64,2}:
1 2 3
4 5 6
julia> cumprod(a,1)
2×3 Array{Int64,2}:
1 2 3
4 10 18
julia> cumprod(a,2)
2×3 Array{Int64,2}:
1 2 6
4 20 120
```
"""
cumprod(A::AbstractArray, axis::Integer=1) = cumprod!(similar(A), A, axis)
cumprod!(B, A) = cumprod!(B, A, 1)
cumsum!(B, A, axis::Integer) = cumop!(+, B, A, axis)
cumprod!(B, A, axis::Integer) = cumop!(*, B, A, axis)
function cumop!(op, B, A, axis::Integer)
axis > 0 || throw(ArgumentError("axis must be a positive integer"))
inds_t = indices(A)
indices(B) == inds_t || throw(DimensionMismatch("shape of B must match A"))
axis > ndims(A) && return copy!(B, A)
isempty(inds_t[axis]) && return B
if axis == 1
# We can accumulate to a temporary variable, which allows
# register usage and will be slightly faster
ind1 = inds_t[1]
@inbounds for I in CartesianRange(tail(inds_t))
tmp = convert(eltype(B), A[first(ind1), I])
B[first(ind1), I] = tmp
for i_1 = first(ind1)+1:last(ind1)
tmp = op(tmp, A[i_1, I])
B[i_1, I] = tmp
end
end
else
R1 = CartesianRange(indices(A)[1:axis-1]) # not type-stable
R2 = CartesianRange(indices(A)[axis+1:end])
_cumop!(op, B, A, R1, inds_t[axis], R2) # use function barrier
end
return B
end
@noinline function _cumop!(op, B, A, R1, ind, R2)
# Copy the initial element in each 1d vector along dimension `axis`
i = first(ind)
@inbounds for J in R2, I in R1
B[I, i, J] = A[I, i, J]
end
# Accumulate
@inbounds for J in R2, i in first(ind)+1:last(ind), I in R1
B[I, i, J] = op(B[I, i-1, J], A[I, i, J])
end
B
end
### from abstractarray.jl
function fill!{T}(A::AbstractArray{T}, x)
xT = convert(T, x)
for I in eachindex(A)
@inbounds A[I] = xT
end
A
end
function copy!{T,N}(dest::AbstractArray{T,N}, src::AbstractArray{T,N})
@boundscheck checkbounds(dest, indices(src)...)
for I in eachindex(linearindexing(src,dest), src)
@inbounds dest[I] = src[I]
end
dest
end
function copy!(dest::AbstractArray, Rdest::CartesianRange, src::AbstractArray, Rsrc::CartesianRange)
isempty(Rdest) && return dest
size(Rdest) == size(Rsrc) || throw(ArgumentError("source and destination must have same size (got $(size(Rsrc)) and $(size(Rdest)))"))
@boundscheck checkbounds(dest, Rdest.start)
@boundscheck checkbounds(dest, Rdest.stop)
@boundscheck checkbounds(src, Rsrc.start)
@boundscheck checkbounds(src, Rsrc.stop)
deltaI = Rdest.start - Rsrc.start
for I in Rsrc
@inbounds dest[I+deltaI] = src[I]
end
dest
end
# circshift!
circshift!(dest::AbstractArray, src, ::Tuple{}) = copy!(dest, src)
"""
circshift!(dest, src, shifts)
Circularly shift the data in `src`, storing the result in
`dest`. `shifts` specifies the amount to shift in each dimension.
The `dest` array must be distinct from the `src` array (they cannot
alias each other).
See also `circshift`.
"""
@noinline function circshift!{T,N}(dest::AbstractArray{T,N}, src, shiftamt::DimsInteger)
dest === src && throw(ArgumentError("dest and src must be separate arrays"))
inds = indices(src)
indices(dest) == inds || throw(ArgumentError("indices of src and dest must match (got $inds and $(indices(dest)))"))
_circshift!(dest, (), src, (), inds, fill_to_length(shiftamt, 0, Val{N}))
end
circshift!(dest::AbstractArray, src, shiftamt) = circshift!(dest, src, (shiftamt...,))
# For each dimension, we copy the first half of src to the second half
# of dest, and the second half of src to the first half of dest. This
# uses a recursive bifurcation strategy so that these splits can be
# encoded by ranges, which means that we need only one call to `mod`
# per dimension rather than one call per index.
# `rdest` and `rsrc` are tuples-of-ranges that grow one dimension at a
# time; when all the dimensions have been filled in, you call `copy!`
# for that block. In other words, in two dimensions schematically we
# have the following call sequence (--> means a call):
# circshift!(dest, src, shiftamt) -->
# _circshift!(dest, src, ("first half of dim1",)) -->
# _circshift!(dest, src, ("first half of dim1", "first half of dim2")) --> copy!
# _circshift!(dest, src, ("first half of dim1", "second half of dim2")) --> copy!
# _circshift!(dest, src, ("second half of dim1",)) -->
# _circshift!(dest, src, ("second half of dim1", "first half of dim2")) --> copy!
# _circshift!(dest, src, ("second half of dim1", "second half of dim2")) --> copy!
@inline function _circshift!(dest, rdest, src, rsrc,
inds::Tuple{AbstractUnitRange,Vararg{Any}},
shiftamt::Tuple{Integer,Vararg{Any}})
ind1, d = inds[1], shiftamt[1]
s = mod(d, length(ind1))
sf, sl = first(ind1)+s, last(ind1)-s
r1, r2 = first(ind1):sf-1, sf:last(ind1)
r3, r4 = first(ind1):sl, sl+1:last(ind1)
tinds, tshiftamt = tail(inds), tail(shiftamt)
_circshift!(dest, (rdest..., r1), src, (rsrc..., r4), tinds, tshiftamt)
_circshift!(dest, (rdest..., r2), src, (rsrc..., r3), tinds, tshiftamt)
end
# At least one of inds, shiftamt is empty
function _circshift!(dest, rdest, src, rsrc, inds, shiftamt)
copy!(dest, CartesianRange(rdest), src, CartesianRange(rsrc))
end
# circcopy!
"""
circcopy!(dest, src)
Copy `src` to `dest`, indexing each dimension modulo its length.
`src` and `dest` must have the same size, but can be offset in
their indices; any offset results in a (circular) wraparound. If the
arrays have overlapping indices, then on the domain of the overlap
`dest` agrees with `src`.
```julia
julia> src = reshape(collect(1:16), (4,4))
4×4 Array{Int64,2}:
1 5 9 13
2 6 10 14
3 7 11 15
4 8 12 16
julia> dest = OffsetArray{Int}((0:3,2:5))
julia> circcopy!(dest, src)
OffsetArrays.OffsetArray{Int64,2,Array{Int64,2}} with indices 0:3×2:5:
8 12 16 4
5 9 13 1
6 10 14 2
7 11 15 3
julia> dest[1:3,2:4] == src[1:3,2:4]
true
```
"""
function circcopy!(dest, src)
dest === src && throw(ArgumentError("dest and src must be separate arrays"))
indssrc, indsdest = indices(src), indices(dest)
if (szsrc = map(length, indssrc)) != (szdest = map(length, indsdest))
throw(DimensionMismatch("src and dest must have the same sizes (got $szsrc and $szdest)"))
end
shift = map((isrc, idest)->first(isrc)-first(idest), indssrc, indsdest)
all(x->x==0, shift) && return copy!(dest, src)
_circcopy!(dest, (), indsdest, src, (), indssrc)
end
# This uses the same strategy described above for _circshift!
@inline function _circcopy!(dest, rdest, indsdest::Tuple{AbstractUnitRange,Vararg{Any}},
src, rsrc, indssrc::Tuple{AbstractUnitRange,Vararg{Any}})
indd1, inds1 = indsdest[1], indssrc[1]
l = length(indd1)
s = mod(first(inds1)-first(indd1), l)
sdf = first(indd1)+s
rd1, rd2 = first(indd1):sdf-1, sdf:last(indd1)
ssf = last(inds1)-s
rs1, rs2 = first(inds1):ssf, ssf+1:last(inds1)
tindsd, tindss = tail(indsdest), tail(indssrc)
_circcopy!(dest, (rdest..., rd1), tindsd, src, (rsrc..., rs2), tindss)
_circcopy!(dest, (rdest..., rd2), tindsd, src, (rsrc..., rs1), tindss)
end
# At least one of indsdest, indssrc are empty (and both should be, since we've checked)
function _circcopy!(dest, rdest, indsdest, src, rsrc, indssrc)
copy!(dest, CartesianRange(rdest), src, CartesianRange(rsrc))
end
### BitArrays
## getindex
# contiguous multidimensional indexing: if the first dimension is a range,
# we can get some performance from using copy_chunks!
@inline function _unsafe_getindex!(X::BitArray, B::BitArray, I0::Union{UnitRange{Int},Colon})
copy_chunks!(X.chunks, 1, B.chunks, indexoffset(I0)+1, index_lengths(B, I0)[1])
return X
end
# Optimization where the inner dimension is contiguous improves perf dramatically
@generated function _unsafe_getindex!(X::BitArray, B::BitArray, I0::Union{Colon,UnitRange{Int}}, I::Union{Int,UnitRange{Int},Colon}...)
N = length(I)
quote
$(Expr(:meta, :inline))
@nexprs $N d->(I_d = I[d])
idxlens = @ncall $N index_lengths B I0 d->I[d]
f0 = indexoffset(I0)+1
l0 = idxlens[1]
gap_lst_1 = 0
@nexprs $N d->(gap_lst_{d+1} = idxlens[d+1])
stride = 1
ind = f0
@nexprs $N d->begin
stride *= size(B, d)
stride_lst_d = stride
ind += stride * indexoffset(I_d)
gap_lst_{d+1} *= stride
end
storeind = 1
Xc, Bc = X.chunks, B.chunks
@nloops($N, i, d->(1:idxlens[d+1]),
d->nothing, # PRE
d->(ind += stride_lst_d - gap_lst_d), # POST
begin # BODY
copy_chunks!(Xc, storeind, Bc, ind, l0)
storeind += l0
end)
return X
end
end
# in the general multidimensional non-scalar case, can we do about 10% better
# in most cases by manually hoisting the bitarray chunks access out of the loop
# (This should really be handled by the compiler or with an immutable BitArray)
@generated function _unsafe_getindex!(X::BitArray, B::BitArray, I::Union{Int,AbstractArray{Int},Colon}...)
N = length(I)
quote
$(Expr(:meta, :inline))
stride_1 = 1
@nexprs $N d->(stride_{d+1} = stride_d*size(B, d))
$(Symbol(:offset_, N)) = 1
ind = 0
Xc, Bc = X.chunks, B.chunks
idxlens = @ncall $N index_lengths B d->I[d]
@nloops $N i d->(1:idxlens[d]) d->(@inbounds offset_{d-1} = offset_d + (I[d][i_d]-1)*stride_d) begin
ind += 1
unsafe_bitsetindex!(Xc, unsafe_bitgetindex(Bc, offset_0), ind)
end
return X
end
end
## setindex!
# contiguous multidimensional indexing: if the first dimension is a range,
# we can get some performance from using copy_chunks!
@inline function setindex!(B::BitArray, X::Union{BitArray,Array}, I0::Union{Colon,UnitRange{Int}})
@boundscheck checkbounds(B, I0)
l0 = index_lengths(B, I0)[1]
setindex_shape_check(X, l0)
l0 == 0 && return B
f0 = indexoffset(I0)+1
copy_to_bitarray_chunks!(B.chunks, f0, X, 1, l0)
return B
end
@inline function setindex!(B::BitArray, x, I0::Union{Colon,UnitRange{Int}})
@boundscheck checkbounds(B, I0)
y = Bool(x)
l0 = index_lengths(B, I0)[1]
l0 == 0 && return B
f0 = indexoffset(I0)+1
fill_chunks!(B.chunks, y, f0, l0)
return B
end
@inline function setindex!(B::BitArray, X::Union{BitArray,Array}, I0::Union{Colon,UnitRange{Int}}, I::Union{Int,UnitRange{Int},Colon}...)
@boundscheck checkbounds(B, I0, I...)
_unsafe_setindex!(B, X, I0, I...)
end
@generated function _unsafe_setindex!(B::BitArray, X::Union{BitArray,Array}, I0::Union{Colon,UnitRange{Int}}, I::Union{Int,UnitRange{Int},Colon}...)
N = length(I)
rangeexp = [I[d] === Colon ? :(1:size(B, $(d+1))) : :(I[$d]) for d = 1:N]
quote
idxlens = @ncall $N index_lengths B I0 d->I[d]
@ncall $N setindex_shape_check X idxlens[1] d->idxlens[d+1]
isempty(X) && return B
f0 = indexoffset(I0)+1
l0 = idxlens[1]
gap_lst_1 = 0
@nexprs $N d->(gap_lst_{d+1} = idxlens[d+1])
stride = 1
ind = f0
@nexprs $N d->begin
stride *= size(B, d)
stride_lst_d = stride
ind += stride * indexoffset(I[d])
gap_lst_{d+1} *= stride
end
refind = 1
Bc = B.chunks
@nloops($N, i, d->$rangeexp[d],
d->nothing, # PRE
d->(ind += stride_lst_d - gap_lst_d), # POST
begin # BODY
copy_to_bitarray_chunks!(Bc, ind, X, refind, l0)
refind += l0
end)
return B
end
end
@inline function setindex!(B::BitArray, x, I0::Union{Colon,UnitRange{Int}}, I::Union{Int,UnitRange{Int},Colon}...)
@boundscheck checkbounds(B, I0, I...)
_unsafe_setindex!(B, x, I0, I...)
end
@generated function _unsafe_setindex!(B::BitArray, x, I0::Union{Colon,UnitRange{Int}}, I::Union{Int,UnitRange{Int},Colon}...)
N = length(I)
rangeexp = [I[d] === Colon ? :(1:size(B, $(d+1))) : :(I[$d]) for d = 1:N]
quote
y = Bool(x)
idxlens = @ncall $N index_lengths B I0 d->I[d]
f0 = indexoffset(I0)+1
l0 = idxlens[1]
l0 == 0 && return B
@nexprs $N d->(isempty(I[d]) && return B)
gap_lst_1 = 0
@nexprs $N d->(gap_lst_{d+1} = idxlens[d+1])
stride = 1
ind = f0
@nexprs $N d->begin
stride *= size(B, d)
stride_lst_d = stride
ind += stride * indexoffset(I[d])
gap_lst_{d+1} *= stride
end
@nloops($N, i, d->$rangeexp[d],
d->nothing, # PRE
d->(ind += stride_lst_d - gap_lst_d), # POST
fill_chunks!(B.chunks, y, ind, l0) # BODY
)
return B
end
end
## findn
@generated function findn{N}(B::BitArray{N})
quote
nnzB = countnz(B)
I = ntuple(x->Array{Int}(nnzB), $N)
if nnzB > 0
count = 1
@nloops $N i B begin
if (@nref $N B i) # TODO: should avoid bounds checking
@nexprs $N d->(I[d][count] = i_d)
count += 1
end
end
end
return I
end
end
## isassigned
@generated function isassigned(B::BitArray, I_0::Int, I::Int...)
N = length(I)
quote
@nexprs $N d->(I_d = I[d])
stride = 1
index = I_0
@nexprs $N d->begin
l = size(B,d)
stride *= l
1 <= I_{d-1} <= l || return false
index += (I_d - 1) * stride
end
return isassigned(B, index)
end
end
## permutedims
## Permute array dims ##
function permutedims(B::StridedArray, perm)
dimsB = size(B)
ndimsB = length(dimsB)
(ndimsB == length(perm) && isperm(perm)) || throw(ArgumentError("no valid permutation of dimensions"))
dimsP = ntuple(i->dimsB[perm[i]], ndimsB)::typeof(dimsB)
P = similar(B, dimsP)
permutedims!(P, B, perm)
end
function checkdims_perm{TP,TB,N}(P::AbstractArray{TP,N}, B::AbstractArray{TB,N}, perm)
indsB = indices(B)
length(perm) == N || throw(ArgumentError("expected permutation of size $N, but length(perm)=$(length(perm))"))
isperm(perm) || throw(ArgumentError("input is not a permutation"))
indsP = indices(P)
for i = 1:length(perm)
indsP[i] == indsB[perm[i]] || throw(DimensionMismatch("destination tensor of incorrect size"))
end
nothing
end
for (V, PT, BT) in [((:N,), BitArray, BitArray), ((:T,:N), Array, StridedArray)]