BONSampler

A union of the abstract types BONSeeder and BONRefiner. Both types return a tuple with the coordinates as a vector of CartesianIndex, and the weight matrix as a Matrix of AbstractFloat, in that order.

AdaptiveSpatial

...

numpoints, an Integer (def. 50), specifying the number of points to use.

α, an AbstractFloat (def. 1.0), specifying ...

abstract type BONRefiner end

A BONRefiner is an algorithm for proposing sampling locations by refining a set of candidate points to a smaller set of 'best' points.

abstract type BONSeeder end

A BONSeeder is an algorithm for proposing sampling locations using a raster of weights, represented as a matrix, in each cell.

BalancedAcceptance

A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)

Uniqueness

A BONRefiner.

entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})

This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).

Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.

entropize(A::Matrix{Number})

Allocation version of entropize!.

refine!(cooords::Vector{CartesianIndex}, pool::Vector{CartesianIndex}, sampler::ST, uncertainty::Matrix{T})

Refines a set of candidate sampling locations in the preallocated vector coords from a vector of coordinates pool using sampler, where sampler is a BONRefiner.

refine!(cooords::Vector{CartesianIndex}, pool::Vector{CartesianIndex}, sampler::ST, uncertainty::Matrix{T})

The curried version of refine!, which returns a function that acts on the input coordinate pool passed to the curried function (p below).

refine(sampler::BONRefiner)

Returns a curried function of refine with two methods: both are using the output of seed, one in its packed form, the other in its splatted form.

refine(pack, sampler::BONRefiner)

Calls refine on the appropriatedly splatted version of pack.

refine(pool::Vector{CartesianIndex}, sampler::ST, uncertainty::Matrix{T})

Refines a set of candidate sampling locations and returns a vector coords of length numpoints from a vector of coordinates pool using sampler, where sampler is a BONRefiner.

seed!(coords::Vector{CartesianIndex}, sampler::ST)

The curried version of seed!, which returns a function that acts on the input uncertainty layer passed to the curried function (u below).

seed!(coords::Vector{CartesianIndex}, sampler::ST, uncertainty::Matrix{T})

Puts a set of candidate sampling locations in the preallocated vector coords from a raster uncertainty using sampler, where sampler is a BONSeeder.

• Seeder's work on rasters, refiners work on set of coordinates.

seed!(coords::Vector{CartesianIndex}, sampler::ST)

The curried version of seed!, which returns a function that acts on the input uncertainty layer passed to the curried function (u below).

seed(sampler::ST, uncertainty::Matrix{T})

Produces a set of candidate sampling locations in a vector coords of length numpoints from a raster uncertainty using sampler, where sampler is a BONSeeder.

squish(layers, W, α)

Takes a set of n layers and squishes them down to a single layer.

    numcolumns = size(W,2)
    for i in 1:numcolumns
        W[:,i] ./= sum(W[:,i])
    end

For a coordinate in the raster (i,j), denote the vector of values across all locations at that coordinate v⃗ᵢⱼ. The value at that coordinate in squished layer, s⃗ᵢⱼ, is computed in two steps.

(1): First we apply a weights matrix, W, withnrows andmcolumns (m<n), to reduce the initialnlayers down to a set ofm` layers, each of which corresponds to a particular target of optimization. For example, we may want to propose sampling locations that are optimized to best sample abalance multiple criteria, like (a) the current distribution of a species and (b) if that distribution is changing over time.

Each entry in the weights matrix W corresponds to the 'importance' of the layer in the corresponding row to the successful measurement of the target of the corresponding column. As such, each column of W must sum to 1.0. using Optim

For each location, the value of the condensed layer tᵢ, corresponding to target i, at coordinate (i,j) is given by the dot product of v⃗ᵢⱼ and the i-th column of W.

(2): Apply a weighted average across each target layer. To produce the final output layer, we apply a weighted average to each target layer, where the weights are provided in the vector α⃗ of length m.

The final value of the squished layer at (i,j) is given by s⃗ᵢⱼ = ∑ₓ αₓ*tᵢⱼ(x), where tᵢⱼ(x) is the value of the x-th target layer at (i,j).