This is the public Git repository containing source code used in J. Gonzalez Nuñez, J. Paulose, W. Möbius, D. A. Beller, "Range Expansions across Landscapes with Quenched Noise." arXiv:2310.11563 (2023).
Example usage is shown below.
A two-parameter sweep can be performed using the src/processing/generate_parameters_space_search.py file. For example,
python src/processing/generate_parameter_space_search.py --numberTrials number_of_independent_runs --numberSamples number_of_sampling_points_for_speed_measure --dims width,height --data_path path_to_save_location --radius radius --density density --intensity intensity --ref_line cutoff_height --detailed_analytics --nEnvs number_of_landscapes --parameters parameter_1,parameter_2 --intervals_1 start,step,stop --intervals_2 start,step,stop --rewrite
will generate number_of_landscapes distinct landscapes and number_of_trials simulations for each landscape, and vary the parameter_1 and parameter_2 parameters for the specified range (interval_1 and interval_2); data will be saved to path_to_save_loation; hotspot parameters are set to density, intensity, and radius (see below); system size is set to width x height; the final-time for the simulation data is set to cutoff_height; measurements of front propgation speed require setting the number of sampling points during a simulation to number_of_sampling_points_for_speed_measure.
Note that parameter_1 and parameter_2 must be one of the following strings: ["density", "intensity", "radius"], for example, --parameters "density","intensity". While values for all three --density, --intensity, --radius can be provided, only the parameter that will not be varied is required, for example "radius"; the other two values, if provided, will be overwritten by values set by interval_1 and interval_2.
A list of simulation options and their descriptions can be found using
python src/processing/generate_parameter_space_search.py --help
The commands used in the above-referenced article are contained in workarea/simulation_commands.txt. There are two sets of commands; the first corresponds to a parameter scan in the low intensity regime, while the second set of commands corresponds to the intermediate to high intensity regime.
Source code used to generate article figures are contained in the figures/ directory. Analysis is performed by providing a list of directories (input variable) containing the simluation data from generate_parameter_space_search.py. If all data are saved in, for example, workarea/experiments/, the figure scripts will automatically scan and collect all simulation data and proceed with the analysis.
There are three pre-processing scripts that need to be executed before running figure scripts: figures/routines/ancestry.jl which builds a n-ary tree using lineage branch points and processes lineage coalescences; src/processing/process_fastest_paths.jl which constructs a hotspot graph and uses a Floyd-Warshall (Dijkstra) path-finding algorithm to find optimal paths; src/processing/process_lineages.jl which measures the number of surviving ancestors, lineage Mean-Square-Displacement, and lineage tortuosity. The command-line options for these scripts can be listed using the --help option.
Figures 3A and 7A in the article use data generated from the first set of commands in workarea/simulation_commands.txt, while figures 3B, 7B, 6C, 6D, and 9 use both sets of data generation commands. Data for figures 4, 6A, 6B, and 8 are selected from both sets using the parameter values shown in the main text.
Additionally, individual simulations can be executed using main.jl; a list of command-line options can be displayed using
julia src/base/main.jl --help
SI figures of optimal path calculations can be generated in the jupyter notebook src/calculations/hotspot-graph.ipynb.
This code requires both Julia (v1.10.4) and python (v3.11.5) to be installed. Our Julia environment is contained in the Manifest.toml and Project.toml files. Our Python environment is provided in requirements.txt and in environment.yml.
Julia package dependencies can be downloaded and installed for the current project using the Project.toml file through the following command:
julia --project=. -e 'using Pkg; Pkg.instantiate()'
If using the Conda package manager for python, the python dependencies can be installed using
conda create --name <env_name> --file requirements.txt
While the script generate_parameter_space_search.py to generate simulations works best with the slurm workload manager installed, the script will check for an existing slurm installion and will fallback to executing sequentially via bash if no slurm installation is found.