... ... @@ -16,49 +16,7 @@ Pkg.add("ABMEv#no_C_matrix") julia using ABMEv  To develop, you can add the dependencies in the project by doing julia using Pkg Pkg.activate("path_to_ABMEv")  ## Birth and Death mechanisms > We are always balanced between taking the integral of the competition and resource kernel as constant, or taking its maximum peak as constant. :poop: ## Geotrait The geotrait is calculated *a posteriori*, and is not taken into account during the simulation. > It used to be but for the sake of simplicity we now forget about it. ### Mutation If anisotropy in mutation, the following parameters should be declared as arrays where each entry corresponds to a dimension. - mu The probability of mutation. - D If mutation happens on the agent, the increment follows $\mathcal{N}_{ 0, D}$ ### Birth #### Growth - Resource kernel for agent with trait $x$ is defined as math K_{\mu,\sigma}(x) = K_0 \mathcal{N}_{\mu,\sigma}(x)  with $\mu$ and $\sigma$ potentially vectors. > We just modified this in ABMEv_Agent.jl so you should check if it works. - Dirth coefficient is defined as $b(x) = K(x)$ ### Death #### Competition - Competition between agent with trait x and y is defined as math \alpha(x,y) = \exp(-\sum_i^{N(t)} \frac{1}{\sigma_{\alpha_i}^{n_\alpha}}\sum_j^T (x_{i,j} - y_{i,j})^{n_\alpha})  - Death coefficient is defined as $d(x^{(i)}) = \sum_j^{N(t)} \alpha(x^{(i)},x^{(j)})$ > We are not sure if the sum includes $x^{(i)}$ or not. ### Fitness Fitness is defined as b - d. ## Parameter description - K0 Carrying capacity - a only used for mode grad2D where growth rate is set such as $\mu = a x_1$ > We are not sure if this is OK or not? Check it [Grad2D kernel explained](https://gitlab.ethz.ch/bvictor/abmev/-/wikis/Grad2D) ### Gillepsie algorithm julia ... ... @@ -104,6 +62,35 @@ You have several options available concerning the resource implemented and compe -  mode="split" corresponds to a scenario where the resource is splitted in two -  mode="graph" this guy is probably not working ### Mutation If anisotropy in mutation, the following parameters should be declared as arrays where each entry corresponds to a dimension. - mu The probability of mutation. - D If mutation happens on the agent, the increment follows $\mathcal{N}_{ 0, D}$ ### Birth #### Growth - Resource kernel for agent with trait $x$ is defined as math K_{\mu,\sigma}(x) = K_0 \mathcal{N}_{\mu,\sigma}(x)  with $\mu$ and $\sigma$ potentially vectors. > We just modified this in ABMEv_Agent.jl so you should check if it works. - Dirth coefficient is defined as $b(x) = K(x)$ ### Death #### Competition - Competition between agent with trait x and y is defined as math \alpha(x,y) = \exp(-\sum_i^{N(t)} \frac{1}{\sigma_{\alpha_i}^{n_\alpha}}\sum_j^T (x_{i,j} - y_{i,j})^{n_\alpha})  - Death coefficient is defined as $d(x^{(i)}) = \sum_j^{N(t)} \alpha(x^{(i)},x^{(j)})$ > We are not sure if the sum includes $x^{(i)}$ or not. ### Fitness Fitness is defined as b - d. ## Parameter description - K0 Carrying capacity - a only used for mode grad2D where growth rate is set such as $\mu = a x_1$ > We are not sure if this is OK or not? Check it [Grad2D kernel explained](https://gitlab.ethz.ch/bvictor/abmev/-/wikis/Grad2D) ### Parallelism You can run your script in parallel, which makes sense for large populations. To do so: ... ... @@ -147,6 +134,16 @@ You can specify what you want to plot in the array what: - 3d plots a 3d diagram with first and second component as x and y axis - var plots the variance of the component specified by trait=2 - vargeo plots the variance of the geotrait ## Developping the code I recommend to first clone your branch in the directory you like best, and then to To develop, you ca julia using Pkg Pkg.dev("path_to_ABMEv_dir") ` You can also do the same trick with directly the gitlab address, cf [https://docs.julialang.org/en/v1/stdlib/Pkg/index.html](Pkg.jl) ## References - [Champagnat and Ferriere founding article](https://linkinghub.elsevier.com/retrieve/pii/S0040580905001632) - [Champagnat and Ferriere second article - 2008](https://www.tandfonline.com/doi/full/10.1080/15326340802437710)