DOE algorithms

Warning

Some algorithms may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped optimization libraries may not be exposed through GEMSEO.

CustomDOE

Module: gemseo.algos.doe.lib_custom

This samples are provided either as a file in text or csv format or as a sequence of sequences of numbers.

Optional parameters
  • comments : str | Sequence[str] | None, optional

    The characters or list of characters used to indicate the start of a comment. None implies no comments.

    By default it is set to #.

  • delimiter : str | None, optional

    The character used to separate values. If None, use whitespace.

    By default it is set to ,.

  • doe_file : str | Path | TextIO | None, optional

    Either a file path or the generator to read. If None, the samples are used and must be provided.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • samples : ndarray | None, optional

    The samples. If None, the doe_file is used and must be provided.

    By default it is set to None.

  • skiprows : int, optional

    The number of first lines to skip.

    By default it is set to 0.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

DiagonalDOE

Module: gemseo.algos.doe.lib_scalable

Diagonal design of experiments

Optional parameters
  • eval_jac : bool, optional

    Whether to evaluate the Jacobian.

    By default it is set to False.

  • max_time : float, optional

    The maximum runtime in seconds. If 0, no maximum runtime is set.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int, optional

    The number of samples. The number of samples must be greater than or equal to 2.

    By default it is set to 2.

  • reverse : Container[str] | None, optional

    The dimensions or variables to sample from their upper bounds to their lower bounds. If None, every dimension will be sampled from its lower bound to its upper bound.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    Additional arguments.

OT_AXIAL

Module: gemseo.algos.doe.lib_openturns

Axial design

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Axial.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_COMPOSITE

Module: gemseo.algos.doe.lib_openturns

Composite design

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Composite.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_FACTORIAL

Module: gemseo.algos.doe.lib_openturns

Factorial design

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Factorial.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_FAURE

Module: gemseo.algos.doe.lib_openturns

Faure sequence

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.FaureSequence.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_FULLFACT

Module: gemseo.algos.doe.lib_openturns

Full factorial design

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Box.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_HALTON

Module: gemseo.algos.doe.lib_openturns

Halton sequence

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaltonSequence.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_HASELGROVE

Module: gemseo.algos.doe.lib_openturns

Haselgrove sequence

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaselgroveSequence.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_LHS

Module: gemseo.algos.doe.lib_openturns

Latin Hypercube Sampling

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHS.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_LHSC

Module: gemseo.algos.doe.lib_openturns

Centered Latin Hypercube Sampling

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHS.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_MONTE_CARLO

Module: gemseo.algos.doe.lib_openturns

Monte Carlo sequence

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.RandomGenerator.html.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_OPT_LHS

Module: gemseo.algos.doe.lib_openturns

Optimal Latin Hypercube Sampling

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SimulatedAnnealingLHS.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_RANDOM

Module: gemseo.algos.doe.lib_openturns

Random sampling

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.RandomGenerator.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_REVERSE_HALTON

Module: gemseo.algos.doe.lib_openturns

Reverse Halton

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.ReverseHaltonSequence.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_SOBOL

Module: gemseo.algos.doe.lib_openturns

Sobol sequence

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolSequence.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

OT_SOBOL_INDICES

Module: gemseo.algos.doe.lib_openturns

DOE for Sobol ‘indices

More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolIndicesAlgorithm.html.

Optional parameters
  • annealing : bool, optional

    If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.

    By default it is set to True.

  • centers : Sequence[int] | None, optional

    The centers for axial, factorial and composite designs. If None, centers = 0.5.

    By default it is set to None.

  • criterion : str, optional

    The space-filling criterion, either “C2”, “PhiP” or “MinDist”.

    By default it is set to C2.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • levels : int | Sequence[int] | None, optional

    The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_replicates : int, optional

    The number of Monte Carlo replicates to optimize LHS.

    By default it is set to 1000.

  • n_samples : int | None, optional

    The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely OpenTURNS.seed.

    By default it is set to None.

  • temperature : str, optional

    The temperature profile for simulated annealing, either “Geometric” or “Linear”.

    By default it is set to Geometric.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

bbdesign

Module: gemseo.algos.doe.lib_pydoe

Box-Behnken design implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/rsm.html#box-behnken.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

ccdesign

Module: gemseo.algos.doe.lib_pydoe

Central Composite implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/rsm.html#central-composite.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

ff2n

Module: gemseo.algos.doe.lib_pydoe

2-Level Full-Factorial implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#level-full-factorial.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

fullfact

Module: gemseo.algos.doe.lib_pydoe

Full-Factorial implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#general-full-factorial.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

lhs

Module: gemseo.algos.doe.lib_pydoe

Latin Hypercube Sampling implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/randomized.html#latin-hypercube.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.

pbdesign

Module: gemseo.algos.doe.lib_pydoe

Plackett-Burman design implemented in pyDOE

More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#plackett-burman.

Optional parameters
  • alpha : str, optional

    A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.

    By default it is set to orthogonal.

  • center_bb : int | None, optional

    The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.

    By default it is set to None.

  • center_cc : tuple[int, int] | None, optional

    The 2-tuple of center points for the central composite design. If None, use (4, 4).

    By default it is set to None.

  • criterion : str | None, optional

    The criterion to use when sampling the points. If None, randomize the points within the intervals.

    By default it is set to None.

  • eval_jac : bool, optional

    Whether to evaluate the jacobian.

    By default it is set to False.

  • face : str, optional

    The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.

    By default it is set to faced.

  • iterations : int, optional

    The number of iterations in the correlation and maximin algorithms.

    By default it is set to 5.

  • levels : Sequence[int] | None, optional

    The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.

    By default it is set to None.

  • max_time : float, optional

    The maximum runtime in seconds, disabled if 0.

    By default it is set to 0.

  • n_processes : int, optional

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • n_samples : int | None, optional

    The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.

    By default it is set to None.

  • seed : int | None, optional

    The seed value. If None, use the seed of the library, namely PyDOE.seed.

    By default it is set to None.

  • wait_time_between_samples : float, optional

    The waiting time between two samples.

    By default it is set to 0.0.

  • **kwargs : OptionType

    The additional arguments.