Internal Documentation

Wraps a Julia Expression generated by generate_dbdt. Mostly useful for pretty-printing, but you can evaluate it like a regular expression. The actual expression lies inside the .expr attribute.

Convert a MultiplexNetwork to a FoodWeb. The convertion consists in removing the non-trophic layers of the multiplex network.

Filter species of the network (net) for which f(species_index, net) = true.

length(n::NutrientIntake)

Number of nutrients in the nutrient intake model.

Transform species of the network (net) by applying f to each species.

One line ModelParameters display.

One line BioRates display.

One line Environment display.

One line FoodWeb display.

One line display FunctionalResponse

One line Layer display.

One line MultiplexNetwork display.

One line display FunctionalResponse

Base.show(io::IO, temperature_response::TemperatureResponse)

One-line TemperatureResponse display.

Multiline ModelParameters display.

Multiline BioRates display.

Multiline BioenergeticResponse display.

Multiline ClassicResponse display.

Multiline Environment display.

Multiline TemperatureResponse::ExponentialBA display.

Multiline FoodWeb display.

Multiline LinearResponse display.

Multiline LogisticGrowth display.

Multiline MultiplexNetwork display.

Multiline NutrientIntake display.

Adjacency matrix of potential links given by the potential_links function in net.

Create species parameter vectors for a, b of length S (species richness) given the allometric parameters for the different metabolic classes (aₚ,aᵢ,...).

Allometric scaling: parameter expressed as a power law of body-mass (M).

asymmetrize(V)

Remove duplicate tuples from a symmetric vector of tuples. A vector V of tuples is said to be symmetric ⟺ ((i,j) ∈ V ⟺ (j,i) ∈ V). The tuple that has its 1st element less than its 2nd element is kept i.e. if i < j (i,j) is kept, and (j,i) otherwise.

See also symmetrize.

Check that labels have the correct format and convert them to Strings if needed.

Check that provided metabolic classes are valid.

Compute consumption terms of ODEs.

dudt!(du, u, p, _)

Compute the species and nutrient (when relevant) abundance derivatives du, given the abundances u and the model p. The last silent argument is the time at which is evaluated the derivatives and is a requirement of DifferentialEquations.

Effect of competition on the net growth rate.

Effect of facilitation on intrinsic growth rate.

Effect of refuge on attack rate.

fill_sparsematrix(scalar, template_matrix)

Return a matrix filled with a constant (scalar) for indexes where the value of the template_matrix is non-zero.

Examples

julia> template_matrix = ones(2, 2);

julia> Internals.fill_sparsematrix(10, template_matrix)
2×2 SparseArrays.SparseMatrixCSC{Float64, Int64} with 4 stored entries:
 10.0  10.0
 10.0  10.0

julia> template_matrix[1, 1] = 0;

julia> Internals.fill_sparsematrix(10, template_matrix)
2×2 SparseArrays.SparseMatrixCSC{Float64, Int64} with 3 stored entries:
   ⋅   10.0
 10.0  10.0

Return the adjacency matrix of the trophic interactions.

Return indices of the invertebrates of the network (net).

Check that net does not contain cycles and does not have disconnected nodes.

Is species i an invertebrate?

Is species i an ectotherm vertebrate?

mass_ratio(p::ModelParameters)

Mean predator-prey body mass ratio given the model parameters.

mass_ratio(network::EcologicalNetwork)

Mean predator-prey body mass ratio given the network.

Generate a food web of S species and number of links L from a structural model. Loop until the generated has a number of links in [L - ΔL; L + ΔL]. If the maximum number of iterations is reached an error is thrown instead.

Generate a food web of S species and connectance C from a structural model. Loop until the generated has connectance in [C - ΔC; C + ΔC].

Number of resources species i is feeding on.

Return a vector where element i is the number of resource(s) of species i.

nutrient_dynamics(model::ModelParameters, B, i_nutrients, n, G)

Compute the dynamics of the nutrient i_nutrient given its abundance n, the species biomass B and the vector of species growths G and the model p.

The nutrient dynamics is applicable only if p is of type NutrientIntake.

Parse pairs within FoodWeb() method working on adjacency list.

Return indices of the predators of the given network.

Return indices of the preys of the network (net).

process_idxs(solution; idxs = nothing)

Check and sanitize the species indices or names provided (idxs). Used in extract_last_timesteps and living_species.

Construct a copy of the expression with the replacements given in rep.

julia> import EcologicalNetworksDynamics.Internals: replace

julia> replace(:(a + (b + c / a)), Dict(:a => 5, :b => 8))
:(5 + (8 + c / 5))

Does the user want to replace 'vertebrates' by 'ectotherm vertebrates'?

Do species i and j share at least one prey?

symmetrize(V)

Add symmetric tuples from an asymmetric vector of tuples. A vector V of tuples is said to be asymmetric ⟺ ((i,j) ∈ V ⇒ (j,i) ∉ V).

See also asymmetrize.

Return indices of the vertebrates of the network (net).

Repeat the given expression into terms of a sum, successively replacing indexes in term by elements in (zipped) lists.

julia> import EcologicalNetworksDynamics.Internals: xp_sum

julia> xp_sum([:i], [[1, 2, 3]], :(u^i)) #  Three terms.
:(u ^ 1 + u ^ 2 + u ^ 3)

julia> xp_sum([:i], [[1]], :(u^i)) #  Single term.
:(u ^ 1)

julia> xp_sum([:i], [[]], :(u^i)) #  No terms.
0

julia> xp_sum([:i, :j], [[:a, :b, :c], [5, 8, 13]], :(j * i)) #  Zipped indices.
:(5a + 8b + 13c)