NaNStatistics.jl
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Fast summary statistics, histograms, and binning – ignoring NaNs
NaNStatistics
Because NaN
is just missing
with hardware support!
Fast (often LoopVectorization-based) summary statistics, histograms, and binning — all ignoring NaN
s, as if NaN
represented missing data.
Summary statistics
Summary statistics exported by NaNStatistics are generally named the same as their normal counterparts, but with "nan" in front of the name, similar to the Matlab and NumPy conventions. Options include:
Reductions
-
nansum
-
nanminimum
-
nanmaximum
-
nanextrema
Measures of central tendency
-
nanmean
arithmetic mean, ignoringNaN
s -
nanmedian
median, ignoringNaN
s -
nanmedian!
asnanmedian
but quicksorts in-place for efficiency
Measures of dispersion
-
nanvar
variance -
nanstd
standard deviation -
nancov
covariance -
nancor
Pearson's product-moment correlation -
nanaad
mean (average) absolute deviation from the mean -
nanmad
median absolute deviation from the median -
nanmad!
asnanmad
but quicksorts in-place for efficiency -
nanrange
range between nanmaximum and nanminimum -
nanpctile
percentile -
nanpctile!
asnanpctile
but quicksorts in-place for efficiency
Note that, regardless of implementation, functions involving medians or percentiles are generally significantly slower than other summary statistics, since calculating a median or percentile requires a quicksort or quickselect of the input array; if not done in-place as in nanmedian!
and nanpctile!
then a copy of the entire array must also be made.
These functions will generally support the same dims
keyword argument as their normal Julia counterparts (though are most efficient when operating on an entire collection).
As an alternative to dims
, the dim
keyword is also supported, which behaves identially to dims
except that it also (as is the norm in some other languages) drops any singleton dimensions that have been reduced over.
julia> a = rand(100000);
julia> @btime minimum($a)
51.950 μs (0 allocations: 0 bytes)
7.630517166790085e-6
julia> using NaNStatistics
julia> @btime nanminimum($a)
19.690 μs (0 allocations: 0 bytes)
7.630517166790085e-6
julia> a[rand(1:100000, 10000)] .= NaN;
julia> @btime nanminimum($a)
19.663 μs (0 allocations: 0 bytes)
7.630517166790085e-6
Histograms
The main 1D and 2D histogram function is histcounts
(with an in-place variant histcounts!
), and will, as you might expect for this package, ignore NaNs. However, it might be worth using for speed even if your data don't contain any NaNs:
julia> b = 10 * rand(100000);
julia> using StatsBase
julia> @btime fit(Histogram, $b, 0:1:10, closed=:right)
2.633 ms (2 allocations: 224 bytes)
Histogram{Int64, 1, Tuple{StepRange{Int64, Int64}}}
edges:
0:1:10
weights: [10128, 10130, 10084, 9860, 9973, 10062, 10003, 10045, 9893, 9822]
closed: right
isdensity: false
julia> using NaNStatistics
julia> @btime histcounts($b, 0:1:10)
1.037 ms (1 allocation: 160 bytes)
10-element Vector{Int64}:
10128
10130
10084
9860
9973
10062
10003
10045
9893
9822
Binning
NaNStatistics also provides functions that will efficiently calculate the summary statistics of a given dependent variable y
binned by an independent variable x
. These currently include:
-
nanbinmean
/nanbinmean!
-
nanbinmedian
/nanbinmedian!
julia> x = 10 * rand(100000);
julia> y = x.^2 .+ randn.();
julia> xmin, xmax, nbins = 0, 10, 10;
julia> @btime nanbinmean($x,$y,xmin,xmax,nbins)
364.082 μs (2 allocations: 320 bytes)
10-element Vector{Float64}:
0.3421697507351903
2.3065542448799015
6.322448227456871
12.340306767007629
20.353233411797074
30.347815506059405
42.31866909140384
56.32256214256441
72.35387230251672
90.35682945641588
Other functions
-
movmean
A simple moving average function, which can operate in 1D or 2D, ignoring NaNs.
julia> A = rand(1:10, 4,4)
4×4 Matrix{Int64}:
3 5 10 3
4 2 5 8
5 6 8 8
2 6 10 6
julia> movmean(A, 3)
4×4 Matrix{Float64}:
3.5 4.83333 5.5 6.5
4.16667 5.33333 6.11111 7.0
4.16667 5.33333 6.55556 7.5
4.75 6.16667 7.33333 8.0
-
nanstandardize
/nanstandardize!
De-mean and set to unit variance