feat: type-stable variable

Project.toml

@@ -30,6 +30,7 @@ IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 
 [targets]
-test = ["Dates", "Test", "Random", "Printf", "IntervalSets"]
+test = ["Dates", "Test", "Random", "Printf", "IntervalSets", "JET"]

src/variable.jl

@@ -75,14 +75,35 @@ end
 
 """
     v = variable(ds::NCDataset,varname::String)
+    v = variable(ds, varname, DType, dimnames)
 
 Return the NetCDF variable `varname` in the dataset `ds` as a
 `NCDataset.Variable`. No scaling or other transformations are applied when the
 variable `v` is indexed.
+
+With the second form, supplying the correct data `DType` (e.g. `Float32`) 
+and `dimnames` (a tuple, e.g. `("lat", "lon")`) can lets you read in a type-stable way.
+
+!!! note
+
+    Incorrectly specified `DType` or `dimnames` can lead to incorrect
+    results (e.g. "garbage" output) with no errors, so proceed with caution.
 """
 variable(ds::NCDataset,varname::AbstractString) = _variable(ds,varname)
 variable(ds::NCDataset,varname::Symbol) = _variable(ds,varname)
 
+function variable(
+    ds::NCDataset, 
+    varname::Union{AbstractString, Symbol}, 
+    DType::DataType, 
+    dimnames::NTuple{N, AbstractString},
+) where {N}
+    dimids = nc_inq_dimid.(ds.ncid, dimnames)
+    varid = nc_inq_varid(ds.ncid, varname)
+    Variable{DType, N, typeof(ds)}(ds, varid, dimids)
+end
+
+
 export variable
 
 

test/test_variable.jl

@@ -3,6 +3,7 @@ using Dates
 using Printf
 using NCDatasets
 using DataStructures
+import JET
 
 sz = (4,5)
 filename = tempname()
@@ -316,3 +317,19 @@ NCDatasets.load!(variable(ds, "temperature"), v, CartesianIndices((1:10,10:30)))
 vv = [1.0f0]
 NCDatasets.load!(variable(ds, "temperature"), vv, CartesianIndex(5,5))
 @test vv[1] == data[CartesianIndex(5,5)]
+
+### Test type stability of `variable(ds, varname, DType, dimnames)`
+JET.@test_opt variable(ds, "temperature", Float32, ("lon", "lat"))
+# Note: Incorrect order of `dimnames` doesn't error if slices are within bounds...
+var1 = variable(ds, "temperature", Float32, ("lon", "lat"))[1:5, 1:10]
+var2 = variable(ds, "temperature", Float32, ("lat", "lon"))[1:5, 1:10]  # TODO: Ensure this errors
+@test var1 == var2
+# ... but incorrect order of `dimnames` and out-of-bounds slices errors
+@test_throws variable(ds, "temperature", Float32, ("lat", "lon"))[:, 1]  # `:` is actually `1:110`
+# incorrect `dimnames` errors
+@test_throws variable(ds, "temperature", Float32, ("lon", "lat", "time"))
+@test_throws variable(ds, "temperature", Float32, ("lon",))
+# Incorrect `DType` doesn't error, but gives incorrect result
+d1 = variable(ds, "temperature", Float32, ("lon", "lat"))[1:5, 1:3]
+d2 = variable(ds, "temperature", Float64, ("lon", "lat"))[1:5, 1:3]
+@test d1 != d2  # TODO: Ensure this errors