Add DiskArrayEngine-based xmap function #502
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something from DAT.jl broke the |
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the project.toml version of this branch is |
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #502 +/- ##
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- Coverage 69.38% 68.79% -0.60%
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Files 12 14 +2
Lines 1813 2096 +283
==========================================
+ Hits 1258 1442 +184
- Misses 555 654 +99
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@meggart ok, these are the main stoppers now (we might actually need those updates to the
And these the tests showing the errors. using Test
using DimensionalData
using DimensionalData: DimensionalData as DD
import NetCDF
using YAXArrays
using YAXArrays: YAXArrays as YAX
d1 = DD.X(1:20)
d2 = DD.Y(1:10)
a1 = rand(20,10)
a2 = rand(20,10)
td = mktempdir()
f1, f2 = joinpath.(td,("file_1.nc","file_2.nc"))
array1 = YAXArray((d1,d2),a1)
array2 = YAXArray((d1,d2),a2)
savecube(array1,f1)
savecube(array2,f2)
ds = open_mfdataset(DD.DimArray([f1,f2],(YAX.time(1:2),)))
@test ds.layer.data[:,:,1] == array1
@test ds.layer.data[:,:,2] == array2
td = mktempdir()
f1, f2 = joinpath.(td,("file_1.nc","file_2.nc"))
td1, td2 = YAX.time(1:2), YAX.time(3:4)
a1,a2 = rand(20,10,2), rand(20,10,2)
array1,array2 = YAXArray((d1,d2,td1),a1), YAXArray((d1,d2,td2),a2)
savecube(array1,f1)
savecube(array2,f2)
ds = open_mfdataset(joinpath(td,"*.nc"))
@test ds.layer.data[:,:,1:2] == array1
@test ds.layer.data[:,:,3:4] == array2and all the tests from @testset "Loop chunk distribution" begin
using DiskArrays: DiskArrays, GridChunks, RegularChunks, IrregularChunks, AbstractDiskArray
using YAXArrayBase: YAXArrayBase
using YAXArrays
struct LargeDiskArray{N,CT<:GridChunks{N}} <: AbstractDiskArray{Float64,N}
size::NTuple{N,Int}
chunks::CT
compressed::Bool
end
Base.size(a::LargeDiskArray) = a.size
DiskArrays.eachchunk(a::LargeDiskArray) = a.chunks
DiskArrays.haschunks(::LargeDiskArray) = DiskArrays.Chunked()
YAXArrayBase.iscompressed(a::LargeDiskArray) = a.compressed
s = (4000,2000,1500)
cs = (100,100,700)
YAXArrays.YAXDefaults.max_cache[] = 1.0e8
a1 = YAXArray(LargeDiskArray(s, GridChunks(s,cs),true))
#Test case where chunk has to be split
dc = mapslices(sum, a1, dims="Dim_1", debug = true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test length(ch) == 2
@test ch[1] == RegularChunks(4,0,2000)
@test ch[2] == RegularChunks(700,0,1500)
dc.outcubes[1].cube
# Test that the allocated buffer is close to what the prescribes size
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0;
#Test subsets and offset
a2 = a1[Dim_3=201..1300.5]
dc = mapslices(sum, a2, dims="Dim_1", debug = true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(4,0,2000), RegularChunks(700,200,1100))
# Test that the allocated buffer is close to what the prescribes size
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0;
#Test with different max_cache
YAXArrays.YAXDefaults.max_cache[] = 2.0e8
dc = mapslices(sum, a2, dims="Dim_1", debug = true)
ch = YAXArrays.DAT.getloopchunks(dc)
#Test loop chunk sizes
@test ch == (RegularChunks(5,0,2000), RegularChunks(700,200,1100))
# Test that the allocated buffer is close to what the prescribes size
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0# Test that the allocated buffer is close to what the prescribes size
# Test case that is chunk-friendly
YAXArrays.YAXDefaults.max_cache[] = 1.5e8
dc = mapslices(sum, a1, dims="Dim_3", debug = true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(100,0,4000), RegularChunks(100,0,2000))
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc)
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0# Test that the allocated buffer is close to what the prescribes size
#With offset
a2 = a1[Dim_1=51..3050.5]
dc = mapslices(sum, a2, dims="Dim_3", debug = true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(100,50,3000), RegularChunks(100,0,2000))
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc)
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0# Test that the allocated buffer is close to what the prescribes size
#With more working memory
YAXArrays.YAXDefaults.max_cache[] = 4.5e8
a2 = a1[Dim_1=51..3050.5]
dc = mapslices(sum, a1, dims="Dim_3", debug = true);
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(300,0,4000), RegularChunks(100,0,2000))
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < (sum(sizeof,incubes) + sum(sizeof,outcubes))/YAXArrays.YAXDefaults.max_cache[] <= 1.0# Test that the allocated buffer is close to what the prescribes size
#Now a completely different test, this is more for DataFrame functionality
# and tests if we can proagate irregular axes into the cubes
using Dates
yearchunks = map(y->isleapyear(y) ? 366 : 365, 2001:2008)
c = GridChunks(RegularChunks(100,0,4000), RegularChunks(100,0,2000), IrregularChunks(chunksizes=yearchunks))
s = last.(last(c))
YAXArrays.YAXDefaults.max_cache[] = 1.0e8
a1 = YAXArray(LargeDiskArray(s, c,true))
dc = mapCube(identity, a1, indims = InDims(), outdims = (), debug=true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(300,0,4000), RegularChunks(100,0,2000), RegularChunks(365,0,2922))
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < sum(sizeof,incubes)/YAXArrays.YAXDefaults.max_cache[] <= 1.0#
dc = mapCube(identity, a1, indims = InDims(), outdims = (), debug=true,irregular_loopranges = true)
ch = YAXArrays.DAT.getloopchunks(dc)
@test ch == (RegularChunks(300,0,4000), RegularChunks(100,0,2000), IrregularChunks(chunksizes=yearchunks))
incubes, outcubes = YAXArrays.DAT.getCubeCache(dc);
@test 0.5 < sum(sizeof,incubes)/YAXArrays.YAXDefaults.max_cache[] <= 1.0#
end |
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JuliaIO/DiskArrays.jl#256 might solve the first issue. |
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now, only the name alternatives? xmap -> DEmap -> demap -> dmap -> Dmap? |
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The loop chunk tests are not relevant anymore and can be deleted. |
ok, but then we should properly delete
afterwards, once |
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Julia versioninfoBaseline resultBenchmark Report for /home/runner/work/YAXArrays.jl/YAXArrays.jlJob Properties
ResultsBelow is a table of this job's results, obtained by running the benchmarks.
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