Simulation control

Trajectory

class l2l.utils.trajectory.Trajectory(**keyword_args)

Bases: object

The trajectory is a class which holds the history of the parameter space exploration, defines the current parameters to be explored and holds the results from each execution. Based on the pypet trajectory concept: https://github.com/SmokinCaterpillar/pypet

f_add_parameter_group(name, comment='')

Adds a new parameter group :param name: name of the new parameter group :param comment: ignored for the moment. Kept to match pypet interface.

f_add_parameter_to_group(group_name, key, val)

Adds a parameter to an already existing group.

Parameters

• group_name – Name of the group where the parameter should be added

• key – Name of the parameter to be added

• val – Value of the parameter

Throws an exception if the group does not exist

f_add_result(key, val, comment='')

Adds a result to the trajectory :param key: it identifies either a generation params result group or another result :param val: The value to be added to the results TODO: verify where is the generation_params call performed

f_add_parameter(key, val, comment='')

Adds a parameter to the trajectory :param key: Name of the parameter :param val: Value of the parameter :param comment

f_add_derived_parameter(key, val, comment='')

Adds a derived parameter to the trajectory. Match the previous pypet interface. :param key: Name of the parameter :param val: Value of the parameter :param comment:

f_expand(build_dict, fail_safe=True)

The expand function takes care of adding a new generation and individuals to the trajectory This is a critical function to allow the addition of a new generation, called by the optimizer from the postprocessing function :param build_dict: The dictionary containing the new generation id and its individuals :param fail_safe: Currently ignored

Individual

class l2l.utils.individual.Individual(generation=0, ind_idx=0, params=[])

Bases: l2l.utils.groups.ParameterGroup

This class represents individuals in the parameter search. It derives from a Parameter group. The main elements which make an individual are the ID of its generation, its individual ID and the params specific for its run.

ParamterGroup

class l2l.utils.groups.ParameterGroup

Bases: object

This class is a Dictionary which can be used to store parameters. It is used to fit the pypet already existing interface with the trajectory

f_add_parameter(key, val, comment='')

Adds parameter with name key and value val. The comment is ignored for the moment but kept for compatibility with the pypet groups :param key: Name of the parameter :param val: Value of the parameter :param comment: Ignores for the moment

Experiment

class l2l.utils.experiment.Experiment(root_dir_path)

Bases: object

prepare_experiment(**kwargs)

Prepare the experiment by creating the environment

Parameters

kwargs –

optional dictionary, contains

• name: str, name of the run, Default: L2L-run

• trajectory_name: str, name of the trajectory, Default: trajectory

• log_stdout: bool, if stdout should be sent to logs, Default:False

• jube_parameter: dict, User specified parameter for jube. See notes section for default jube parameter

• multiprocessing, bool, enable multiprocessing, Default: False

Returns

traj, trajectory object

Returns

all_jube_params, dict, a dictionary with all parameters for jube given by the user and default ones

Notes

Default JUBE parameters are:

• scheduler: None,

• submit_cmd: sbatch,

• job_file: job.run,

• nodes: 1,

• walltime: 01:00:00,

• ppn: 1,

• cpu_pp: 1,

• threads_pp: 4,

• mail_mode: ALL,

• err_file: stderr,

• out_file: stdout,

• tasks_per_job: 1,

• exec: python3 + self.paths.simulation_path + “run_files/run_optimizee.py”

• ready_file: self.paths.root_dir_path + “ready_files/ready_w_”

• work_path: self.paths.root_dir_path,

• paths_obj: self.paths

run_experiment(optimizer, optimizee, optimizer_parameters=None, optimizee_parameters=None)

Runs the simulation with all parameter combinations

Optimizee and optimizer object are required as well as their parameters as namedtuples.

Parameters

• optimizee – optimizee object

• optimizee_parameters – Namedtuple, optional, parameters of the optimizee

• optimizer – optimizer object

• optimizer_parameters – Namedtuple, optional, parameters of the optimizer

end_experiment(optimizer)

Ends the experiment and disables the logging

Parameters

optimizer – optimizer object

Returns

traj, trajectory object

Returns

path, Path object