optim_results

class nrv.optim.optim_utils.optim_results(context=None)

Container class storing optimization parameters, history, and best solution data.

Attributes

optim_results.is_dummy	Indicate whether the results object is a dummy placeholder.
optim_results.is_empty	Indicate whether the results contain a declared result type.
optim_results.to_save	Indicate whether the results object should be serialized.

Methods

optim_results.__init__([context])	Initialize an optimization-results container.
optim_results.add_filter(part_filter)	Apply a post-processing filter to every stored particle position.
optim_results.clear()
optim_results.compute_best_pos(...)	Recompute simulation results at the recorded best position.
optim_results.copy()
optim_results.findbestpart([decimals, ...])	Find which particle reached the recorded best position.
optim_results.fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
optim_results.get(key[, default])	Return the value for key if key is in the dictionary, else default.
optim_results.get_parameters()	Generic method returning all the atributes of an NRV_class instance
optim_results.is_stabilized(part, it[, ...])	Check whether one particle velocity has fallen below a stabilization threshold.
optim_results.items()
optim_results.keys()
optim_results.load(data[, blacklist])	Load optimization results and reserialize the nested optimization parameters.
optim_results.plot_cost_history(ax[, ...])	Plot the optimization cost history.
optim_results.pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
optim_results.popitem()	Remove and return a (key, value) pair as a 2-tuple.
optim_results.remove_key([keys_to_remove, ...])	Remove a key or a list of keys from the results
optim_results.save([save, fname, blacklist])	Save the results object after synchronizing its internal state.
optim_results.set_parameters(**kawrgs)	Generic method to set any attribute of NRV_class instance
optim_results.setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
optim_results.stabilization_it([parts, nit, ...])	Estimate the last iteration at which selected particles were not yet stabilized.
optim_results.update(_NRV_results__m, **kwargs)	overload of dict update method to update both attibute and items
optim_results.values()