Update dependencies (#103)

* bump deps

* fix tests

* test clarify

Project.toml

@@ -1,7 +1,7 @@
 name = "ClimateBase"
 uuid = "35604d93-0fb8-4872-9436-495b01d137e2"
 authors = ["Datseris <[email protected]>", "Philippe Roy <[email protected]>"]
-version = "0.16.3"
+version = "0.16.4"
 
 [deps]
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
@@ -15,8 +15,8 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
-DimensionalData = "0.20.1, 0.21, 0.22, 0.23"
-Interpolations = "0.13.2"
+DimensionalData = "0.20.1, 0.21, 0.22, 0.23, 0.24"
+Interpolations = "0.13.2, 0.14"
 NCDatasets = "0.11, 0.12"
 Requires = "1"
 SignalDecomposition = "1"

test/advanced_tests.jl

@@ -6,8 +6,8 @@
     my_dim = Dim{:My_Dim}
     E = ClimArray([1.0:1.0:10.0 2.0:1.0:11.0 3.0:2.0:21.0], (my_dim(1:10), Ti(1:3)))
     pressure = ClimArray([1000.0:-100.0:100.0 1000.0:-100.0:100.0 1000.0:-100.0:100.0] *100.0, (my_dim(1:10), Ti(1:3)))
-    E_pre = interpolation2pressure(E, pressure, pressure_levels; vertical_coord=my_dim, extrapolation_bc=NaN )
-    E_pre2 = interpolation2pressure(reverse(E,dims=my_dim), reverse(pressure,dims=my_dim), pressure_levels; vertical_coord=my_dim, extrapolation_bc=NaN, descending = false )
+    E_pre = interpolation2pressure(E, pressure, pressure_levels; vertical_dim=my_dim, extrapolation_bc=NaN )
+    E_pre2 = interpolation2pressure(reverse(E,dims=my_dim), reverse(pressure,dims=my_dim), pressure_levels; vertical_dim=my_dim, extrapolation_bc=NaN, descending = false )
 
     @test hasdim(D_pre,Pre())
     @test gnv(dims(D_back,Hei)) == gnv(dims(D,Hei)) && gnv(dims(D_back,Ti)) == gnv(dims(D,Ti))
@@ -18,10 +18,6 @@
     @test D[Hei(1)] < D_pre[Pre(3)] < D[Hei(11)]
 end
 
-@testset "quantile space" begin
-
-end
-
 @testset "value space" begin
     t = 0:0.01:5π
     x = cos.(t)

test/io_tests.jl

@@ -1,3 +1,5 @@
+using ClimateBase, Test
+
 @testset "NetCDF IO" begin
 
 @testset "vector2range" begin
@@ -57,7 +59,7 @@ end
     @test all(isequal(-99.9), B.data[midx])
     mmean = mean(skipmissing(M2.data))
     bmean = mean(B, StatsBase.weights(W))
-    @test bmean == mmean
+    @test bmean ≈ mmean # for some reason here we lose accuracy: 0.4992455417474702 vs 0.49924554174747
     # test `missing_weights` application to zonal mean
     C = B[3:end, :]
     z1 = zonalmean(C)
@@ -67,7 +69,7 @@ end
 end
 
 @testset "CFTime dates" begin
-    using NCDatasets.CFTime: DateTime360Day
+    using ClimateBase.NCDatasets.CFTime: DateTime360Day
     cfdates = collect(DateTime360Day(1900,01,01):Day(1):DateTime360Day(1919,12,30))
     x = float.(month.(cfdates))
     X = ClimArray(x, (Tim(cfdates),); name = "x")