# Options for DOE algorithms¶

## CustomDOE¶

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

More details about the algorithm and its options on None.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

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 (Optional[str])

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

By default it is set to ,.

• doe_file (Optional[Union[str,)

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)

Whether to evaluate the jacobian.

By default it is set to False.

• max_time (float)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• samples (Optional[ndarray])

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

By default it is set to None.

• skiprows (int)

The number of first lines to skip.

By default it is set to 0.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## DiagonalDOE¶

Diagonal design of experiments

More details about the algorithm and its options on None.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• eval_jac (bool)

Whether to evaluate the Jacobian.

By default it is set to False.

• max_time (float)

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

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (int)

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

By default it is set to 2.

• reverse (Optional[Container[str]])

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)

By default it is set to 0.0.

## OT_AXIAL¶

Axial design

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_COMPOSITE¶

Composite design

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_FACTORIAL¶

Factorial design

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_FAURE¶

Faure sequence

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_FULLFACT¶

Full factorial design

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_HALTON¶

Halton sequence

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_HASELGROVE¶

Haselgrove sequence

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_LHS¶

Latin Hypercube Sampling

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_LHSC¶

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.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_MONTE_CARLO¶

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.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_OPT_LHS¶

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.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_RANDOM¶

Random sampling

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_REVERSE_HALTON¶

Reverse Halton

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_SOBOL¶

Sobol sequence

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## OT_SOBOL_INDICES¶

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.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• annealing (bool)

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

By default it is set to True.

• centers (Optional[Sequence[int]])

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

By default it is set to None.

• criterion (str)

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

By default it is set to C2.

• eval_jac (bool)

Whether to evaluate the jacobian.

By default it is set to False.

• levels (Optional[int,Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_replicates (int)

The number of Monte Carlo replicates to optimize LHS.

By default it is set to 1000.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• temperature (str)

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

By default it is set to Geometric.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## bbdesign¶

Box-Behnken design implemented in pyDOE

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## ccdesign¶

Central Composite implemented in pyDOE

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## ff2n¶

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.

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## fullfact¶

Full-Factorial implemented in pyDOE

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## lhs¶

Latin Hypercube Sampling implemented in pyDOE

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.

## pbdesign¶

Plackett-Burman design implemented in pyDOE

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

Here are the options available in GEMSEO:

Options
• **kwargs (OptionType)

• alpha (str)

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

By default it is set to orthogonal.

• center_bb (Optional[int])

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 (Optional[Tuple[int,)

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

By default it is set to None.

• criterion (Optional[str])

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)

Whether to evaluate the jacobian.

By default it is set to False.

• face (str)

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)

The number of iterations in the correlation and maximin algorithms.

By default it is set to 5.

• levels (Optional[Sequence[int]])

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)

The maximum runtime in seconds, disabled if 0.

By default it is set to 0.

• n_processes (int)

The number of processes.

By default it is set to 1.

• n_samples (Optional[int])

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)

The seed value.

By default it is set to 1.

• wait_time_between_samples (float)

The waiting time between two samples.

By default it is set to 0.0.