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 ... ... @@ -37,11 +37,12 @@ gplot(g, collect(1:nodes), collect(1:nodes)) ![delta_comp_pos](../assets/tutorials/line.png) ## Defining competition processes We propose that any individual have a constant birth rate, and competes with all the individuals present in the same patch. Let i \in \N,x_i \in \{1,2,\dots,9\}. The competition pressure experience by individual i is such that We propose that any individual have a constant birth rate, and competes with all the individuals present in the same patch. Assume there are N_t individuals at time t. Let i \in \{ 1,2,\dots,N_t\}. x_{i,t} \in \{1,2,\dots,9\} denotes the position of the i-th individual at time t. The competition pressure experienced by individual i is such that math d(x_i) = \sum_j \delta(x_i-x_j) d(x_{i,t}) = \sum_j^{N(t)} \delta(x_{i,t}-x_{j,t})  where \delta is the dirac function. ... ... @@ -87,9 +88,6 @@ Let's verify that the population's growth is logistic. We will plot the populati To do so, one need to define dt_saving < tend to save every dt_saving time steps of the world. `julia myagents = [Agent(wholespace,(5,),ancestors=true,rates=true) for i in 1:K0/nodes] w0 = World(myagents,wholespace,p,0.) # we need to reinitialise the world @time sim = run!(w0,Gillepsie(),tend,dt_saving=2.) wsize = [length(w) for w in sim[:]] using Plots Plots.plot(get_tspan(sim),wsize, ... ...
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