partial_sampling_spatially_structured_population

Repository for my lab immersion on spatially structured populations with partial updates.

Using the formalism from "Bridging Wright–Fisher and Moran models".

Code overview

slurm_jobs contains scripts for SCITAS clusters.

utils contains the more elementary scripts:

• graph_generation.py: creates the migration matrices based on the specified type and parameters

• graph_simulation.py: launches graph-based simulations

• misc.py: miscellaneous, among which a failed attempt to compute the theoretical mean extinction time (from equation S32 of the paper)

• wm_mat.py: computing the fixation probability using the transition (well-mixed case)

• wm_sim.py: launches simulations in the well-mixed case

run_single.py and slurm_main.py are used to generate and store simulation data (see part below on how to use them).

visualization.py is used to produce plots.

run.ipynb is a notebook using all the functions to launch simulations and produce plots (the last part is not functional).

Run simulations / computations

slurm_main.py

Runs multiple simulations / computations of the specified type, sweeping the fitness in np.logspace(log_s_min, log_s_max, num) and writes a .JSON file in the specified results directory.

You can run it as a script (which you must modify to specify prefix, results_dir, num; useful when you want to run them on SCITAS clusters) or as a function (see examples in run.ipynb).

Make sure the results directory is created before running

It is not possible to store the fixation/extinction times. (use run_single.py instead)

Function

Arguments:

• prefix: str, beginning of the filename, e.g. 'simA'

• results_dir: str, path where the results should be written, e.g. 'results/simA/' (must end with a '/')

• num: int, number of fitness values in the logspace array

• type: str, ['wm_sim', 'wm_mat', 'clique', 'cycle', 'line', 'star']

• job_array_nb: int, identifier (useful for running array jobs with slurm too), will be added in the filename

• N: int, number of individuals per deme

• M: int, number of updated individuals per deme

• log_s_min: int,

• log_s_max: int,

• (optional) nb_trajectories: int = 100, number of simulation runs

• (optional) migration_rate: float = 0.001,

• (optional) nb_demes: int = 1, number of demes,

• (optional) alpha: float = 1., migration assymetry (if type is 'cycle', 'line' or 'star')

• (optional) initial_node: int = 0, node where the first mutant spawns (between 0 and nb_demes - 1), or makes an average if initial_node = 'avg' (for line and star graphs)

Returns nothing, writes {prefix}_{type}_{job_array_nb}_{parameters}.json in the results directory

Script

Template:

py slurm_main.py [type] [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

The arguments in parentheses depend on the chosen type:

• For clique graphs: py slurm_main.py clique [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories] (migration_rate) (nb_demes)

• For cycle graphs: py slurm_main.py cycle [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories] (migration_rate) (nb_demes) (alpha)

• For star graphs: py slurm_main.py star [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

• For line graphs: py slurm_main.py line [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

You can choose to have the averaged fixation probability over the possible initial nodes if initial node = 'avg' (for line and star graphs).

• For well-mixed simulations: py slurm_main.py wm_sim [job_array_nb] [N] [M] [log_s_min] [log_s_max] [nb_trajectories]

• For well-mixed matrix computations: py slurm_main.py wm_mat [job_array_nb] [N] [M] [log_s_min] [log_s_max]

run_single.py

Runs multiple simulations / computations of the specified type, using a single fitness value and writes a .JSON file in the specified results directory

You can run it as a script (which you must modify to specify prefix or results_dir; useful when you want to run them on SCITAS clusters) or as a function (see examples in run.ipynb)

Make sure the results directory is created before running

Here, it is possible to store the fixation/extinction times.

Function

Runs multiple simulations / computations of the specified type, sweeping the fitness in np.logspace(log_s_min, log_s_max, num) and writes a .JSON file in the specified results directory

Make sure the results directory is created before running

Arguments:

• prefix: str, beginning of the filename, e.g. 'simA'

• results_dir: str, path where the results should be written, e.g. 'results/simA/' (must end with a '/')

• type: str, ['wm_sim', 'wm_mat', 'clique', 'cycle', 'line', 'star']

• job_array_nb: int, identifier (useful for running array jobs with slurm too), will be added in the filename

• N: int, number of individuals per deme

• M: int, number of updated individuals per deme

• s: float, relative fitness

• (optional) nb_trajectories: int = 100, number of simulation runs

• (optional) migration_rate: float = 0.

• (optional) nb_demes: int = 1, number of demes,

• (optional) alpha: float = 1., migration assymetry (if type is 'cycle', 'line' or 'star')

• (optional) initial_node: int = 0, node where the first mutant spawns (between 0 and nb_demes - 1), or makes an average if initial_node = 'avg' (for line and star graphs)

Returns nothing, writes {prefix}_single_{type}_{job_array_nb}_{parameters}.json in the results directory

Script

Template:

py run_single.py [type] [job_array_nb] [N] [M] [s] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

The arguments in parentheses depend on the chosen type:

• For clique graphs: py run_single.py clique [job_array_nb] [N] [M] [s] [nb_trajectories] (migration_rate) (nb_demes)

• For cycle graphs: py run_single.py cycle [job_array_nb] [N] [M] [s] [nb_trajectories] (migration_rate) (nb_demes) (alpha)

• For star graphs: py run_single.py star [job_array_nb] [N] [M] [s] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

• For line graphs: py run_single.py line [job_array_nb] [N] [M] [s] [nb_trajectories] (migration_rate) (nb_demes) (alpha) (initial node)

• For well-mixed simulations: py run_single.py wm_sim [job_array_nb] [N] [M] [s] [nb_trajectories]

• For well-mixed matrix computations: py run_single.py wm_mat [job_array_nb] [N] [M] [s]