# \[ANN\] Sliding-Window Fast Local (Pearson) Correlation Coefficients

**URL:** https://discourse.julialang.org/t/ann-sliding-window-fast-local-pearson-correlation-coefficients/63720
**Category:** Package Announcements
**Tags:** package, announcement, statistics
**Created:** [June 29, 2021, 1:32am UTC](https://discourse.julialang.org/t/ann-sliding-window-fast-local-pearson-correlation-coefficients/63720 "2021-06-29T01:32:51Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![pitsianis](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pitsianis/32/26588_2.png) [@pitsianis](https://discourse.julialang.org/u/pitsianis)
#### Post date: [June 29, 2021, 1:32am UTC](https://discourse.julialang.org/t/ann-sliding-window-fast-local-pearson-correlation-coefficients/63720/1 "2021-06-29T01:32:51Z")

</div>

Calculate the local (Pearson) correlation coefficients between a `needle` and all sliding windows of the same size within a `haystack`. The best match is where the coefficient is the largest. Find the `needle` even when your copy is scaled and translated. The implementation supports any dimension tensors of reals or complex values.

```julia
julia> using FastLocalCorrelationCoefficients

julia> haystack = rand(ComplexF32,2^5,2^5,2^5,2^5);

julia> needle = rand(1) .* haystack[7:8,1:2,5:6,2:3] .+ rand(1);

julia> c = flcc(haystack,needle);

julia> best_correlated(c)
CartesianIndex(7, 1, 5, 2)

```

When you need to search for many needles of the same size,

```julia
  haystack = rand(2^20);
  needle1 = rand(1) .* haystack[2:8] .+ rand(1);
  needle2 = rand(1) .* haystack[42:48] .+ rand(1);
  needle3 = rand(1) .* haystack[end-6:end] .+ rand(1);

```

you can preprocess the `haystack` to avoid redundant computations by precomputing all common information. Such preprocessing is not possible when using the direct method.

```julia
  precomp = flcc(haystack,size(needle1));

```

Then use it for much faster queries.

```julia
  best_correlated(flcc(precomp,needle1)) == 2
  best_correlated(flcc(precomp,needle2)) == 42
  best_correlated(flcc(precomp,needle3)) == 2^20-6

```

The computation is performed in the Fourier domain, so the complexity is O(n\_H \log(n\_H)), instead of O(n\_H n\_N), where n\_H and n\_N denote the number of entries in the `haystack` and `needle`, respectively.

Give it a try!