support for maskingvalue

docs/src/fillvalue.md

@@ -41,12 +41,12 @@ var_nan = nomissing(ds["var"][:,:],NaN)
 close(ds)
 ```
 
-Such substitution can also be made more automatically using the experimental parameter` _experimental_missing_value` (suggestion for a good name for this parameter are welcome, please make a [github issue](https://github.com/Alexander-Barth/NCDatasets.jl/issues/188)) that can be user per variable:
+Such substitution can also be made more automatically using the experimental parameter` maskingvalue` that can be user per variable:
 
 
 ```julia
 ds = NCDataset("example.nc","r")
-ncvar_nan = cfvariable(ds,"var",_experimental_missing_value = NaN)
+ncvar_nan = cfvariable(ds,"var",maskingvalue = NaN)
 ncvar_nan[:,:]
 # 2×3 Matrix{Float64}:
 #   1.0   2.0   3.0
@@ -57,15 +57,15 @@ close(ds)
 Or per data-set:
 
 ```julia
-ds = NCDataset("example.nc","r", _experimental_missing_value = NaN)
+ds = NCDataset("example.nc","r", maskingvalue = NaN)
 ds["var"][:,:]
 # 2×3 Matrix{Float64}:
 #   1.0   2.0   3.0
 # NaN    20.0  30.0
 close(ds)
 ```
 
-Note choosing the `_experimental_missing_value` affects the element type of the NetCDF variable using julia type promotion rules, in particular note that following vector:
+Note choosing the `maskingvalue` affects the element type of the NetCDF variable using julia type promotion rules, in particular note that following vector:
 
 
 ```julia
@@ -98,7 +98,7 @@ defVar(ds,"var32",data32,("lon","lat"),fillvalue = 9999f0)
 defVar(ds,"var64",data64,("lon","lat"),fillvalue = 9999.)
 close(ds)
 
-ds = NCDataset("example_float32_64.nc","r", _experimental_missing_value = NaN32)
+ds = NCDataset("example_float32_64.nc","r", maskingvalue = NaN32)
 ds["var32"][:,:]
 # 2×3 Matrix{Float32}:
 #   1.0   2.0   3.0

src/NCDatasets.jl

@@ -47,7 +47,7 @@ import CommonDataModel: AbstractDataset, AbstractVariable,
     SubDataset,
     @select, select, Near, coordinate_value, coordinate_names, split_by_and,
     chunking, deflate, checksum,
-    _experimental_missing_value
+    maskingvalue
 
 
 import DiskArrays

src/dataset.jl

@@ -28,7 +28,7 @@ const jlType = Dict(
 const ncType = Dict(value => key for (key, value) in jlType)
 
 iswritable(ds::NCDataset) = ds.iswritable
-_experimental_missing_value(ds::NCDataset) = ds._experimental_missing_value
+maskingvalue(ds::NCDataset) = ds.maskingvalue
 
 function isopen(ds::NCDataset)
     try
@@ -66,7 +66,7 @@ function NCDataset(ncid::Integer,
                    iswritable::Bool,
                    isdefmode::Ref{Bool};
                    parentdataset = nothing,
-                   _experimental_missing_value = missing,
+                   maskingvalue = missing,
                    )
 
     function _finalize(ds)
@@ -75,14 +75,14 @@ function NCDataset(ncid::Integer,
             close(ds)
         end
     end
-    @debug "_experimental_missing_value" _experimental_missing_value
-    ds = NCDataset{typeof(parentdataset),typeof(_experimental_missing_value)}(
+    @debug "maskingvalue" maskingvalue
+    ds = NCDataset{typeof(parentdataset),typeof(maskingvalue)}(
         parentdataset,
         ncid,
         iswritable,
         isdefmode,
         Dict{String,String}(),
-        _experimental_missing_value,
+        maskingvalue,
     )
 
     if !iswritable
@@ -174,7 +174,7 @@ function NCDataset(filename::AbstractString,
                    diskless::Bool = false,
                    persist::Bool = false,
                    memory::Union{Vector{UInt8},Nothing} = nothing,
-                   _experimental_missing_value = missing,
+                   maskingvalue = missing,
                    attrib = [])
 
     ncid = -1
@@ -234,7 +234,7 @@ function NCDataset(filename::AbstractString,
     iswritable = mode != "r"
     ds = NCDataset(
         ncid,iswritable,isdefmode,
-        _experimental_missing_value = _experimental_missing_value)
+        maskingvalue = maskingvalue)
 
     # set global attributes
     for (attname,attval) in attrib

src/types.jl

@@ -24,7 +24,7 @@ struct Variable{NetCDFType,N,TDS} <: AbstractNCVariable{NetCDFType, N}
 end
 
 # must be mutable to register a finalizer
-mutable struct NCDataset{TDS,T_experimental_missing_value} <: AbstractNCDataset where TDS <: Union{AbstractNCDataset,Nothing}
+mutable struct NCDataset{TDS,Tmaskingvalue} <: AbstractNCDataset where TDS <: Union{AbstractNCDataset,Nothing}
     # parent_dataset is nothing for the root dataset
     parentdataset::TDS
     ncid::Cint
@@ -35,7 +35,7 @@ mutable struct NCDataset{TDS,T_experimental_missing_value} <: AbstractNCDataset
     # mapping between variables related via the bounds attribute
     # It is only used for read-only datasets to improve performance
     _boundsmap::Dict{String,String}
-    _experimental_missing_value::T_experimental_missing_value
+    maskingvalue::Tmaskingvalue
 end
 
 const Dataset = NCDataset

test/test_copyvar.jl

@@ -2,32 +2,32 @@ using Dates
 using NCDatasets
 using Test
 
-var = 10.0:10.0:40.0
-tax = DateTime(2001,1,1) .+ Day.(Int.(var))
+datavar = 10.0:10.0:40.0
+tax = DateTime(2001,1,1) .+ Day.(Int.(datavar))
 fname = tempname()
 fname2 = tempname()
 ds = NCDataset(fname, "c")
 defDim(ds, "time", Inf) # "unlimited"
 defVar(ds, "time", tax, ("time",))
-defVar(ds, "var", var, ("time",),deflatelevel=9)
+defVar(ds, "datavar", datavar, ("time",),deflatelevel=9)
 close(ds)
 
 NCDataset(fname, "r") do ds
     time = ds["time"]
-    var = ds["var"]
+    datavar = ds["datavar"]
     NCDataset(fname2, "c") do ds2
         defVar(ds2, "time", time, ("time",))
-        defVar(ds2, "var",  var,  ("time",))
+        defVar(ds2, "datavar",  datavar,  ("time",))
         @test "time" in unlimited(ds)
   end
 end
 
 NCDataset(fname, "r") do ds
     NCDataset(fname2, "c") do ds2
         defVar(ds2, ds["time"])
-        defVar(ds2, ds["var"])
+        defVar(ds2, ds["datavar"])
         @test "time" in unlimited(ds)
-        isshuffled,isdeflated,deflatelevel = deflate(ds["var"])
+        isshuffled,isdeflated,deflatelevel = deflate(ds["datavar"])
         @test deflatelevel == 9
   end
 end

test/test_fillvalue.jl

@@ -182,14 +182,14 @@ defDim(ds,"lat",size(data,2))
 ncv = defVar(ds,"data",Float64,("lon","lat"),fillvalue = fv)
 ncv.var[:,:] = data
 
-ncv = cfvariable(ds,"data",_experimental_missing_value = NaN)
+ncv = cfvariable(ds,"data",maskingvalue = NaN)
 @test eltype(ncv) == Float64
 @test ncv[1,1] == data[1,1]
 @test isnan(ncv[2,2])
 close(ds)
 
 
-ds = NCDataset(fname,"r",_experimental_missing_value = NaN)
+ds = NCDataset(fname,"r",maskingvalue = NaN)
 ncv = ds["data"]
 
 @test ncv[1,1] == data[1,1]