pystra.ddo.DDO#

class DDO(*, study, criterion, objective=None)[source]#

Bases: object

Evaluate a decision context with an objective and acceptability criterion.

Methods

`best_feasible`	Return the feasible row with the largest objective value.
`evaluate`	Return a dataframe with reliability, cost, and decision columns.
`feasible_results`	Return evaluated alternatives satisfying the criterion.
`getResults`	Return the results from the most recent `run()` call.
`lqi`	Create a DDO study using the LQI criterion.
`maximize_unconstrained_objective`	Return the row with the largest objective value before feasibility.
`optimize`	Return the best feasible design alternative.
`plot`	Plot results from the most recent run.
`run`	Evaluate the DDO study.

classmethod lqi(study, *, objective=None, criterion=None, country=None, indexed=None, swtp=None, expected_fatalities_given_failure=None, marginal_safety_cost=None, variability=None, consequence=None)[source]#

Create a DDO study using the LQI criterion.

Return type:: DDO

evaluate()[source]#

Return a dataframe with reliability, cost, and decision columns.

Return type:: DataFrame

run()[source]#

Evaluate the DDO study.

run is provided as a convenience for users familiar with Pystra’s analysis objects. It returns the same dataframe as evaluate().

Return type:: DataFrame

getResults()[source]#

Return the results from the most recent run() call.

Return type:: DataFrame

maximize_unconstrained_objective()[source]#

Return the row with the largest objective value before feasibility.

Return type:: Series

feasible_results()[source]#

Return evaluated alternatives satisfying the criterion.

Return type:: DataFrame

best_feasible()[source]#

Return the feasible row with the largest objective value.

Return type:: Series

optimize()[source]#

Return the best feasible design alternative.

Return type:: Series

plot(design=None, quantities=None, **kwargs)[source]#: Plot results from the most recent run.