def run_kwarg_scan(ModelClass, model_kwarg_sets,
t_output_every, t_upto,
force_resume=True, parallel=False):
"""Run many models with the same parameters but variable `field`.
For each `val` in `vals`, a new model will be made, and run up to a time.
The output directory is automatically generated from the model arguments.
Parameters
----------
ModelClass: type
A class or factory function that returns a model object by
calling `ModelClass(model_kwargs)`
model_kwarg_sets: list[dict]
List of argument sets, each of which can instantiate a model.
t_output_every: float
see :class:`Runner`.
t_upto: float
Run each model until the time is equal to this
parallel: bool
Whether or not to run the models in parallel, using the Multiprocessing
library. If `True`, the number of concurrent tasks will be equal to
one less than the number of available cores detected.
"""
task_runner = _TaskRunner(ModelClass, t_output_every, t_upto, force_resume)
run_func(task_runner, model_kwarg_sets, parallel)
def resume_runs(dirnames, t_output_every, t_upto, parallel=False):
"""Resume many models, and run.
Parameters
----------
dirnames: list[str]
List of output directory paths from which to resume.
output_every: int
see :class:`Runner`.
t_upto: float
Run each model until the time is equal to this
parallel: bool
Whether or not to run the models in parallel, using the Multiprocessing
library. If `True`, the number of concurrent tasks will be equal to
one less than the number of available cores detected.
"""
run_model_partial = partial(run_model, t_output_every, force_resume=True,
t_upto=t_upto)
run_func(run_model_partial, dirnames, parallel)