Make EarthDataLab work for DimDataYAXArrays (#298)

* First tests pass

* Access and analysis ok

* All tests pass

* All tests pass

* bump minimum julia version

Author: meggart

.github/workflows/CI.yml

@@ -13,8 +13,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
           - '1'
+          - '1.8'
         os:
           - ubuntu-latest
           - macOS-latest

Project.toml

@@ -1,11 +1,12 @@
 name = "EarthDataLab"
 uuid = "359177bc-a543-11e8-11b7-bb015dba3358"
 authors = ["Fabian Gans <[email protected]>"]
-version = "0.12.1"
+version = "0.13.0"
 
 [deps]
 CFTime = "179af706-886a-5703-950a-314cd64e0468"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 DiskArrayTools = "fcd2136c-9f69-4db6-97e5-f31981721d63"
 DiskArrays = "3c3547ce-8d99-4f5e-a174-61eb10b00ae3"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
@@ -32,9 +33,9 @@ Polynomials = "1, 2.0, 3"
 StatsBase = "0.32, 0.33, 0.34"
 Tables = "0.2, 1.0"
 WeightedOnlineStats = "0.5, 0.6"
-YAXArrays = "0.4"
+YAXArrays = "0.5"
 Zarr = "0.9"
-julia = "1.6"
+julia = "1.8"
 
 [extras]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

src/EarthDataLab.jl

@@ -12,23 +12,28 @@ include("esdc.jl")
 include("ESDLTools.jl")
 
 using YAXArrays: @reexport
+import DimensionalData as DD
 
 @reexport using Dates: Date, DateTime
 @reexport using YAXArrays: (..)
 @reexport using YAXArrays: concatenatecubes, caxes,
   subsetcube, readcubedata,renameaxis!, YAXArray
-@reexport using YAXArrays: CubeAxis, RangeAxis, CategoricalAxis,
-  getAxis
 
 @reexport using YAXArrays: mapCube, getAxis, InDims, OutDims, Dataset,
       CubeTable, cubefittable, fittable, savecube, Cube, open_dataset #From DAT module
 @reexport using .Proc: removeMSC, gapFillMSC,normalizeTS,
   getMSC, filterTSFFT, getNpY,interpolatecube,
-  getMedSC, extractLonLats, cubefromshape,
+  getMedSC, cubefromshape,
   gapfillpoly, spatialinterp #From Proc module # from ESDL Tools
 
 @reexport using .ESDC: esdc, esdd
 
+#For backwards compatibility:
+RangeAxis(name,vals) = DD.Dim{Symbol(name)}(vals)
+CategoricalAxis(name,vals) = DD.Dim{Symbol(name)}(vals)
+
+export RangeAxis, CategoricalAxis
+
 using NetCDF: NetCDF
 using Zarr: Zarr
 

src/MSC.jl

@@ -124,7 +124,7 @@ Returns the mean annual cycle from each time series.
 """
 function getMSC(c;kwargs...)
   N = getNpY(c)
-  outdims = OutDims(RangeAxis("MSC",DateTime(1900):Day(ceil(Int,366/N)):DateTime(1900,12,31,23,59,59)), 
+  outdims = OutDims(DD.Dim{:MSC}(DateTime(1900):Day(ceil(Int,366/N)):DateTime(1900,12,31,23,59,59)), 
   outtype = mscouttype(eltype(c)))
   indims = InDims("Time")
   mapCube(getMSC,c,getNpY(c);indims=indims,outdims=outdims,kwargs...)
@@ -174,7 +174,7 @@ Returns the median annual cycle from each time series.
 """
 function getMedSC(c;kwargs...)
   N = getNpY(c)
-  outdims = OutDims(RangeAxis("MSC",DateTime(1900):Day(ceil(Int,366/N)):DateTime(1900,12,31,23,59,59)), 
+  outdims = OutDims(DD.Dim{:MSC}(DateTime(1900):Day(ceil(Int,366/N)):DateTime(1900,12,31,23,59,59)), 
   outtype = mscouttype(eltype(c)))
   indims = InDims("Time")
   mapCube(getMedSC,c;indims=indims,outdims=outdims,kwargs...)

src/Proc.jl

@@ -1,9 +1,8 @@
 module Proc
 using YAXArrays.Cubes: YAXArray, cubechunks, caxes
-using YAXArrays.Cubes.Axes: getAxis, findAxis, CategoricalAxis, axVal2Index,
-  RangeAxis, get_bb, axisfrombb, CubeAxis, axname
 using YAXArrays.DAT: mapCube, InDims, OutDims, NValid, AnyMissing
 using YAXArrays.Datasets: getsavefolder, Cube
+using YAXArrays: getAxis
 using Dates: year, Date, DateTime
 """
     getNpY(cube)
@@ -12,7 +11,7 @@ Get the number of time steps per year
 """
 function getNpY(cube)
     timax = getAxis("Time",cube)
-    years = year.(timax.values)
+    years = year.(timax.val)
     years[end] > years[1] + 1 || error("Must have at least 3 years to estimate number of time steps per year")
     return count(i -> i == years[1] + 1, years)
 end
@@ -22,6 +21,5 @@ include("Stats.jl")
 include("TSDecomposition.jl")
 include("remap.jl")
 include("Shapes.jl")
-include("extractlonlats.jl")
 
 end

src/extractlonlats.jl

@@ -1,14 +1,16 @@
 using DiskArrayTools: InterpolatedDiskArray, DiskArrayTools
 using DiskArrayTools.Interpolations: Linear, Flat, Constant, NoInterp
 using DiskArrays: eachchunk, GridChunks, approx_chunksize, grid_offset
+using YAXArrays: findAxis, getAxis
+import DimensionalData as DD
 """
   spatialinterp(c,newlons::AbstractRange,newlats::AbstractRange;order=Linear(),bc = Flat())
 """
 function spatialinterp(c,newlons::AbstractRange,newlats::AbstractRange;order=Linear(),bc = Flat())
   interpolatecube(c,Dict("Lon"=>newlons, "Lat"=>newlats), order = Dict("Lon"=>order,"Lat"=>order))
 end
-spatialinterp(c,newlons::CubeAxis,newlats::CubeAxis;kwargs...)=
-  spatialinterp(c,newlons.values,newlats.values;kwargs...)
+spatialinterp(c,newlons::DD.Dim,newlats::DD.Dim;kwargs...)=
+  spatialinterp(c,newlons.val.data,newlats.val.data;kwargs...)
 
 
 function getsteprat(newax::AbstractRange, oldax::AbstractRange)
@@ -30,7 +32,7 @@ end
     chunkold = eachchunk(c.data)
     axold = caxes(c)
     axinds  = map(i->findAxis(i,c),k)
-    steprats = map((inew,iold)->getsteprat(newaxdict[inew], axold[iold].values),k,axinds)
+    steprats = map((inew,iold)->getsteprat(newaxdict[inew], axold[iold].val.data),k,axinds)
     newcs = ntuple(ndims(c)) do i
         ii = findfirst(isequal(i),axinds)
         round(Int,approx_chunksize(chunkold.chunks[i]) * (ii==nothing ? 1 : steprats[ii]))
@@ -54,7 +56,7 @@ function interpolatecube(c,
     if ai === nothing
       nothing,NoInterp(),nothing,nothing
     else
-      oldvals = caxes(c)[i].values
+      oldvals = caxes(c)[i].val.data
       newvals = newaxes[ii[ai][1]]
       getinterpinds(oldvals, newvals),get(order,ii[ai][1],Constant()),get(bc,ii[ai][1],Flat()),newvals
     end
@@ -68,7 +70,7 @@ function interpolatecube(c,
     if val === nothing
       ax
     else
-      RangeAxis(axname(ax),val)
+      DD.rebuild(ax,val)
     end
   end
   YAXArray(newax,ar,c.properties, cleaner = c.cleaner)

src/remap.jl

@@ -2,6 +2,7 @@
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 DiskArrayTools = "fcd2136c-9f69-4db6-97e5-f31981721d63"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 EarthDataLab = "359177bc-a543-11e8-11b7-bb015dba3358"

test/Project.toml

@@ -3,35 +3,35 @@ using Dates
 c=Cube()
 
 @testset "Access single variable" begin
-d = subsetcube(c,variable="air_temperature_2m",lon=(10,11),lat=(51,50),
-                time=(Date("2002-01-01"),Date("2008-12-31")))
-
-d.data.v.indices==(18:21, 17:20, 93:414)
-@test d.axes[1].values==10.125:0.25:10.875
-@test d.axes[2].values==50.875:-0.25:50.125
-@test d.axes[1] == RangeAxis("lon", 10.125:0.25:10.875)
-@test d.axes[2] == RangeAxis("lat", 50.875:-0.25:50.125)
+  d = c[variable=DD.At("air_temperature_2m"),lon=10..11,lat=50..51,
+  time=Date("2002-01-01")..Date("2008-12-31")]
+
+  d.data.v.indices==(18:21, 17:20, 93:414)
+  @test d.axes[1].val==10.125:0.25:10.875
+  @test d.axes[2].val==50.875:-0.25:50.125
+  @test d.axes[1] == Dim{:lon}(10.125:0.25:10.875)
+  @test d.axes[2] == Dim{:lat}(50.875:-0.25:50.125)
 end
 
 
 
 @testset "Access multiple variables" begin
-d2 = subsetcube(c,variable=["air_temperature_2m","gross_primary_productivity"],lon=(10,11),lat=(50,51),
-                time=(Date("2002-01-01"),Date("2008-12-31")))
-
-  @test d2.axes[4].values == ["air_temperature_2m", "gross_primary_productivity"]
-  @test d2.data.arrays[1].v.indices ==(18:21, 17:20, 93:414)
-  @test d2.axes[1].values==10.125:0.25:10.875
-  @test d2.axes[2].values==50.875:-0.25:50.125
-  @test first(d2.axes[3].values) == Dates.Date(2002,1,5)
-  @test last(d2.axes[3].values) == Dates.Date(2008, 12, 30)
+  d2 = c[variable=DD.At(["air_temperature_2m","gross_primary_productivity"]),lon=10..11,lat=50..51,
+  time=Date("2002-01-01")..Date("2008-12-31")]
+
+@test d2.axes[4].val == ["air_temperature_2m", "gross_primary_productivity"]
+@test d2.data.arrays[1].v.indices ==(18:21, 17:20, 93:414)
+@test d2.axes[1].val==10.125:0.25:10.875
+@test d2.axes[2].val==50.875:-0.25:50.125
+@test first(d2.axes[3].val) == Dates.Date(2002,1,5)
+@test last(d2.axes[3].val) == Dates.Date(2008, 12, 30)
 end
 
 @testset "Test values in MemCube" begin
-d = subsetcube(c,variable="air_temperature_2m",lon=(10,11),lat=(51,50),
-                time=(Date("2002-01-01"),Date("2008-12-31")))
-d2 = subsetcube(c,variable=["air_temperature_2m","gross_primary_productivity"],lon=(10,11),lat=(50,51),
-                time=(Date("2002-01-01"),Date("2008-12-31")))
+  d = c[variable=DD.At("air_temperature_2m"),lon=10..11,lat=50..51,
+  time=Date("2002-01-01")..Date("2008-12-31")]
+d2 = c[variable=DD.At(["air_temperature_2m","gross_primary_productivity"]),lon=10..11,lat=50..51,
+  time=Date("2002-01-01")..Date("2008-12-31")]
 data1=readcubedata(d)
 data2=readcubedata(d2)
 
@@ -40,52 +40,42 @@ data2=readcubedata(d2)
 
 
 @test isapprox(data1.data[1,1,1:10],Float32[267.9917, 269.9631, 276.71036, 280.88998,
-  280.90665, 277.02243, 274.5466, 276.919, 279.96243, 279.42276])
+280.90665, 277.02243, 274.5466, 276.919, 279.96243, 279.42276])
 
 @test isapprox(data2.data[1,1,1:10,1],Float32[267.9917, 269.9631, 276.71036, 280.88998,
-  280.90665, 277.02243, 274.5466, 276.919, 279.96243, 279.42276])
+280.90665, 277.02243, 274.5466, 276.919, 279.96243, 279.42276])
 
-@test caxes(data1)[1:2]==CubeAxis[RangeAxis("lon",10.125:0.25:10.875),RangeAxis("lat",50.875:-0.25:50.125)]
+@test caxes(data1)[1:2]==(Dim{:lon}(10.125:0.25:10.875),Dim{:lat}(50.875:-0.25:50.125))
 
 tax = caxes(data1)[3]
-@test YAXArrays.Cubes.Axes.axsym(tax)==:time
-@test tax.values[1] == Date(2002,1,5)
-@test tax.values[end] == Date(2008,12,30)
-@test length(tax.values) == 7*46
+@test DD.name(tax)==:Time
+@test tax.val[1] == Date(2002,1,5)
+@test tax.val[end] == Date(2008,12,30)
+@test length(tax.val) == 7*46
 
-@test caxes(data2)[[1,2,4]]==CubeAxis[RangeAxis("lon",10.125:0.25:10.875),RangeAxis("lat",50.875:-0.25:50.125),CategoricalAxis("Variable",["air_temperature_2m","gross_primary_productivity"])]
+@test caxes(data2)[[1,2,4]]==(Dim{:lon}(10.125:0.25:10.875),Dim{:lat}(50.875:-0.25:50.125),Dim{:Variable}(["air_temperature_2m","gross_primary_productivity"]))
 
 tax = caxes(data2)[3]
-@test YAXArrays.Cubes.Axes.axsym(tax)==:time
-@test tax.values[1] == Date(2002,1,5)
-@test tax.values[end] == Date(2008,12,30)
-@test length(tax.values) == 7*46
+@test DD.name(tax)==:Time
+@test tax.val[1] == Date(2002,1,5)
+@test tax.val[end] == Date(2008,12,30)
+@test length(tax.val) == 7*46
 
-end
 
-@testset "Coordinate extraction" begin
-d = subsetcube(c,variable="air_temperature_2m",lon=(10,11),lat=(51,50),
-                time=(Date("2002-01-01"),Date("2008-12-31")))
-data1=readcubedata(d)
-# Test reading of coordinate list
-ll=[10.1 50.2;10.5 51.1;10.8 51.1]
-llcube = readcubedata(extractLonLats(data1,ll))
-@test llcube.data[1,:]==data1.data[1,4,:]
-@test llcube.data[2,:]==data1.data[3,1,:]
-@test llcube.data[3,:]==data1.data[4,1,:]
 end
 
 @testset "Accessing regions" begin
 #Test access datacube by region
-d3 = subsetcube(c,variable="gross_primary_productivity",region="Austria",time=Date("2005-01-01"))
-@test d3.axes==[RangeAxis("lon",9.625:0.25:14.875),RangeAxis("lat",48.875:-0.25:47.375)]
+d3 = c[variable=DD.At("gross_primary_productivity"),region="Austria",time=DD.Near(DateTime("2005-01-01"))]
+@test d3.axes==(Dim{:lon}(9.625:0.25:14.875),RangeAxis("lat",48.875:-0.25:47.375))
+
 end
 
 using DiskArrayTools: DiskArrayStack
 
 @testset "Saving and loading cubes" begin
-  d = subsetcube(c,variable="air_temperature_2m",lon=(10,31),lat=(51,50),
-                  time=(Date("2002-01-01"),Date("2008-12-31")))
+  d = c[variable=DD.At("air_temperature_2m"),lon=(10..31),lat=(50..51),
+                  time=(Date("2002-01-01")..Date("2008-12-31"))]
   data1=readcubedata(d)
   #Test saving cubes
   dire=tempname()
@@ -97,7 +87,7 @@ using DiskArrayTools: DiskArrayStack
   @test data1.data==data3.data
 
   # Test loadOrGenerate macro
-  d=subsetcube(c,time=Date(2001)..Date(2005),lon=(10,11),lat=(50,51),variable=["gross_primary_productivity","net_ecosystem_exchange"])
+  d=c[time=Date(2001)..Date(2005),lon=(10..11),lat=(50..51),variable=DD.At(["gross_primary_productivity","net_ecosystem_exchange"])]
 
   danom = removeMSC(d)
 
@@ -108,7 +98,7 @@ using DiskArrayTools: DiskArrayStack
   danom=readcubedata(danom)
   danom2=readcubedata(Cube(zp))
 
-  @test danom.axes==danom2.axes
+  @test danom.axes[1:3]==danom2.axes[1:3]
   @test all(map(isequal,danom.data,danom2.data))
 
   ncf = string(tempname(),".nc")
@@ -118,8 +108,8 @@ using DiskArrayTools: DiskArrayStack
   #Test exportcube
   @test ncread(ncf,"lon") == 10.125:0.25:10.875
   @test ncread(ncf,"lat") == 50.875:-0.25:50.125
-  @test ncgetatt(ncf,"time","units") == "days since 1980-01-01"
-  @test getAxis("time",danom).values .- DateTime(1980) == Millisecond.(Day.(ncread(ncf,"time")))
+  @test ncgetatt(ncf,"Time","units") == "days since 1980-01-01"
+  @test getAxis("Time",danom).val .- DateTime(1980) == Millisecond.(Day.(ncread(ncf,"Time")))
 
   anc = replace(ncread(ncf,"gross_primary_productivity")[:,:,:],-9999.0=>missing)
   @test all(isequal.(anc, danom.data[:,:,:,1]))
@@ -131,9 +121,9 @@ end
 
 @testset "ESDC v3" begin
   c = esdd(res="low")
-  d = c.gross_primary_productivity[time=Date(2005)][443:444,139:140]
+  d = c.gross_primary_productivity[time=DD.Near(DateTime(2005))].data[443:444,139:140]
   d == Float32[3.1673577 3.7342484; 3.3267372 4.0305696]
 
   c = esdd(res="tiny")
-  c.gross_primary_productivity[time=Date(2005)][44,14] == 2.3713999f0
+  c.gross_primary_productivity[time=DD.Near(DateTime(2005))].data[44,14] == 2.3713999f0
 end