API

Compute the C2 count in an image. Serves as a starting point for a reconstruction

C2_pdist4(im, maxrng::Int64, SN::Array{Float64,1}=Array{Float64}(undef, 0))

Fast total computation of the C2 count for a

Normalise S2 count into S2 proper by dividing by BN and ϕ

Find the S2 count for an image

blas_stat4(indx, step::Float64, maxrng)::Array{Int64,1}

Computes a very fast multithreaded probabilistic S2 count. Employs kernel density estimation in place of a true histogram. Gives hilarious results when used for reconstruction.

Compute the S2 count (multithreaded, O(N) memory)

For a list of N CartesianIndex tuples, compute and bin the L2 norm between all pixel pairs in the list.

blas_stat_st2(indx, step, maxrng)

For a set of N points, compute the unique distance between all possible pairs of points. Bins distance measurements with a histogram of bin width step and length maxrng Single threaded implementation, greedy with memory. Good for smaller computations

Prevent fetch_locale from colliding with image edges by providing a reduced window if an edge is collided with

cluster_stat(clusters, step::Float64, maxrng)

Fast compution of S2 count for an array of arrays of cartesian indices. Multithreaded in the sense that

Sloppy computation to find the contribution to C2 count from the old state and the new, and hence the difference between them.

Compute a binary disk-shaped structure element given radius r on the basis of (areal) interpolation

Fetch the region around a pixel selection; give a reduced window if near an edge

Computes equivalent position of unique entry in a shortlist to an entry in a master list. Uses a much more sensible tupled pick

Examine which clusters are present in multiple windows simultaneously to determine which clusters are due to change from a pixel swap

Compute the L2 norm between points (xi, yi) and the arrays (arrx, arry) and write to preallocated array dist

Fuses clusters to reduce the scale of the problem

get_rand_pix((pix))::Tuple{Array{CartesianIndex{2},1},Array{CartesianIndex{2},1}}

Select a random white and black pixel from a tupled pair of lists of cartesian coordinates

Find all unique values present in a small window into the region adjacent to a pixel selection. Used to interrogate a cluster labelled image

inner_blas2(x, y, dist, len)

compute the lower triangular self-interaction matrix of a list of N items. gives a flat vector of F32, of length (N(N-1)/2). Interaction here is computing the L2 norm, but could be otherwise.

Loads and thresholds an image

make_rand_im(philen, dims)

create a random binary array of size dims with philen white pixels set within it. Also return a coordinate list for white and black pixels

Generate isotropically distributed circles that may overlap. Keep the implied phase fraction ϕ below 0.3 if possible

Eliminates NaN and Inf in an array

nonzero(image)

evaluate where an image is nonzero

Selects random elements in a pair of lists

pre_proc(im)

convolve image with a disk-shaped structure element

Deduct the contribution of a given set of pixels from a previously calculated S2 count

Get list of all surface pixels from a 2d binary image using convolution to discriminate Sanitised version to prevent the function returning nothing

Get list of all surface pixels from a 2d binary image using convolution to discriminate

Find the intersection between two tupled lists of cartesian indices We use this to create a segment of a list that maintains ordering

Update tuple of 2 cartesian indices by swapping elements from each list at linear index wpick (for 1) and bpick (for 0)

Computes the C2 count change between a pair of images with one pixel swapped between them

Compute the update to S2 count. Find the contribution from all white pixel's distance from wpick and bpick, and the difference between them