idmtools_calibra.iteration_state module#

class idmtools_calibra.iteration_state.IterationState(**kwargs)[source]#

Bases: object

Holds the settings, parameters, simulation state, analysis results, etc. for one calibtool iteration.

Allows for the resumption or extension of existing CalibManager instances from an arbitrary point in the iterative process.

property status#
update(**kwargs)[source]#
restore_results(iteration)[source]#

Restore summary results from serialized state. :param iteration: the # of iteration

Returns:

run()[source]#
starting_step()[source]#
commission_step()[source]#
analyze_step()[source]#
plotting_step()[source]#
next_point_step()[source]#
commission_iteration(next_params)[source]#

Commission an experiment of simulations constructed from a list of combinations of random seeds, calibration sites, and the next sample points. Cache the relevant experiment and simulation information to the IterationState. :param next_params: the next sample

Returns: None

plot_iteration()[source]#
analyze_iteration()[source]#

Analyze the output of completed simulations by using the relevant analyzers by site. Cache the results that are returned by those analyzers.

wait_for_finished(init_sleep=1.0, sleep_time=30)[source]#
cancel()[source]#
property iteration_directory#
property iteration_file#
property param_names#
finished()[source]#

The next-point algorithm has reached its termination condition.

classmethod from_file(filepath)[source]#
to_file()[source]#
classmethod restore_state(iteration)[source]#

Restore IterationState

set_samples_for_iteration(samples, next_point)[source]#
save()[source]#

Cache information about the IterationState that is needed to resume after an interruption. If resuming from an existing iteration, also copy to backup the initial cached state.

get_parameter_sets_with_likelihoods()[source]#