build based on 6322582

dev/.documenter/index.md

@@ -23,7 +23,7 @@ abstract type BONSampler end
 A `BONSampler` is any algorithm for proposing a set of sampling locations.
 
 
-[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/types.jl#L1-L5)
+[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/types.jl#L1-L5)
 
 </details>
 
@@ -80,7 +80,7 @@ BalancedAcceptance
 A `BONSeeder` that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)
 
 
-[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/balancedacceptance.jl#L1-L6)
+[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/balancedacceptance.jl#L1-L6)
 
 </details>
 
@@ -105,7 +105,7 @@ Uniqueness
 A `BONSampler`
 
 
-[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/uniqueness.jl#L1-L5)
+[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/uniqueness.jl#L1-L5)
 
 </details>
 
@@ -132,7 +132,7 @@ This function turns a matrix `A` (storing measurement values) into pixel-wise en
 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.
 
 
-[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/entropize.jl#L1-L13)
+[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/entropize.jl#L1-L13)
 
 </details>
 
@@ -149,7 +149,7 @@ entropize(A::Matrix{Number})
 Allocation version of `entropize!`.
 
 
-[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/entropize.jl#L30-L34)
+[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/entropize.jl#L30-L34)
 
 </details>
 

dev/.documenter/vignettes/entropize.md

@@ -23,7 +23,7 @@ measurements = rand(MidpointDisplacement(), (200, 200)) .* 100
 heatmap(measurements)
 ```
 
-![](ahvfugp.png)
+![](miqllmb.png)
 
 Using the `entropize` function will convert these values into entropy at the pixel scale: 
 

dev/.documenter/vignettes/overview.md

@@ -17,7 +17,7 @@ U = rand(MidpointDisplacement(0.5), (100, 100))
 heatmap(U)
 ```
 
-![](zqpmxhf.png)
+![](jndzxmv.png)
 
 In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes.
 

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