SentinelMissings.jl

Author meggart
Popularity
7 Stars
Updated Last
2 Years Ago
Started In
December 2018

Build Status

SentinelMissings.jl

This small package is an attempt to deal with data where missing values are represented through a so-called sentinel value. For example, you have an array

x = [0.1,0.2,-9999.0]

where the -9999.0 represents missing data. We can reinterpret this array without copying:

julia> xs = as_sentinel(x,-9999.0)
3-element reinterpret(SentinelMissings.SentinelMissing{Float64,-9999.0}, ::Array{Float64,1}):
     0.1
     0.2
 missing

all operations will promote the SentinelMissing type to a Union{T,Missing} through Julias type promotion system.

julia> xs .- 0.1
3-element Array{Union{Missing, Float64},1}:
 0.0
 0.1
  missing

Although conversion to a SentinelMissing is defined as well:

julia> xs[2]=missing;x
3-element Array{Float64,1}:
     0.1
 -9999.0
 -9999.0

Mmap-example

This is an example how to use SentinelMissings with Mmap:

x = [1 2 3;
  4 5 6;
  -1 -1 10]
open("./mmap.bin","w") do f
    write(f,x)
end
using Mmap
xm = open("./mmap.bin","r+") do f
    Mmap.mmap(f, Matrix{Int}, (3,3))
end
xs = as_sentinel(xm,-1)
3×3 reinterpret(SentinelMissings.SentinelMissing{Int64,-1}, ::Array{Int64,2}):
       1        2   3
       4        5   6
 missing  missing  10

You can do some operations:

any(ismissing,xs,dims=1)
1×3 Array{Bool,2}:
 true  true  true

Still there is no copy, the array is just reinterpreted, so that xs and xm point to the same file:

xs[:,3] = missing
xs
3×3 reinterpret(SentinelMissings.SentinelMissing{Int64,-1}, ::Array{Int64,2}):
       1        2  missing
       4        5  missing
 missing  missing  missing
xm
3×3 Array{Int64,2}:
  1   2  -1
  4   5  -1
 -1  -1  -1